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Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i. i. d. and general non-i. i. d. stochastic models in detail including Markov hidden Markov state-space regression and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms. | Sequential Analysis Hypothesis Testing and Changepoint Detection

GBP 44.99
1

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies including clinical trials. Theoretical statisticians medical researchers and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics epidemiology health-related sciences and areas pertaining to formal decision-making mechanisms.

GBP 44.99
1

Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook with each chapter covering traditional topics such as descriptive statistics regression and hypothesis testing. However unlike a traditional textbook each chapter will present its material using a complete step-by-step analysis of a real publicly available dataset with an emphasis on the practical skills of testing assumptions data exploration and forming conclusions. The chapters in the main body of the book include a worked example showing the R code used at each step followed by a multi-part project for students to complete. These projects which could serve as alternatives to traditional discrete homework problems will illustrate how to put the pieces together and conduct a complete start-to-finish data analysis using the R statistical software package. At the end of the book there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class. Key features of the text: Organized in chapters focusing on the same topics found in typical introductory statistics textbooks (descriptive statistics regression two-way tables hypothesis testing for means and proportions etc. ) so instructors can easily pair this supplementary material with course plans Includes student projects for each chapter which can be assigned as laboratory exercises or homework assignments to supplement traditional homework Features real-world datasets from scientific publications in the fields of history pop culture business medicine and forensics for students to analyze Allows students to gain experience working through a variety of statistical analyses from start to finish The book is written at the undergraduate level to be used in an introductory statistical methods course or subject-specific research methods course such as biostatistics or research methods for psychology or business analytics. Author After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath. com.

GBP 48.99
1

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices. It discusses various standards including those from the ISO MIL-STD and ASTM and explores how to set quality levels. The book also includes problems at the end of each chapter with solutions. This edition improves upon the previous editions especially in the areas of software applications and compliance sampling plans. New to the Third Edition: Numerous Microsoft Excel templates to address sampling plans are used. Commercial software applications are discussed at the end of many chapters. Discussion of quick switching systems has been expanded to account for the considerable recent activity in this area. Added discussion of zero acceptance number chained quick switching systems.

GBP 115.00
1

Teaching Mathematics at a Technical College

Teaching Mathematics at a Technical College

Not much has been written about technical colleges especially teaching mathematics at one. Much had been written about community college mathematics. This book addresses this disparity. Mathematics is a beautiful subject worthy to be taught at the technical college level. The author sheds light on technical colleges and their importance in the higher education system. Technical colleges area more affordable for students and provide many career opportunities. These careers are becoming or have become as lucrative as careers requiring a four-year-degree. The interest in technical college education is likely to continue to grow. Mathematics like all other classes is a subject that needs time energy and dedication to learn. For an instructor it takes many years of hard work and dedication just to be able to teach the subject. Students should not be expected to learn the mathematics overnight. As instructors we need to be open honest and put forth our very best to our students so that they can see that they are able to succeed in whatever is placed in front of them. This book hopes to encourage such an effort. A notable percentage of students who are receiving associate degrees will go through at least one of more mathematics courses. These students should not be forgotten about—their needs are similar to any student who is required to take a mathematics course to earn a degree. This book offers insight into teaching mathematics at a technical college. It is also a source for students to turn toward when they are feeling dread in taking a mathematics course. Mathematics instructors want to help students succeed. If they put forth their best effort and us ours we can all work as one team to get the student through the course and onto chasing their dreams. Though this book focuses on teaching mathematics some chapters expand to focus on teaching in general. The overall hope is the reader will be inspired by the great work that is happening at technical colleges all around the country. Technical college can be should be and is the backbone of the American working class.

GBP 22.99
1

Software Engineering for Science

Software Engineering for Science

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software including not only computational modeling but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains. About the EditorsJeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He is one of the primary organizers of the workshop series on Software Engineering for Science (http://www. SE4Science. org/workshops). Neil P. Chue Hong is Director of the Software Sustainability Institute at the University of Edinburgh. His research interests include barriers and incentives in research software ecosystems and the role of software as a research object. George K. Thiruvathukal is Professor of Computer Science at Loyola University Chicago and Visiting Faculty at Argonne National Laboratory. His current research is focused on software metrics in open source mathematical and scientific software.

GBP 44.99
1

The Weibull Distribution A Handbook

The Weibull Distribution A Handbook

The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics The Weibull Distribution: A Handbook focuses on its origin statistical properties and related distributions. The book also presents various approaches to estimate the parameters of the Weibull distribution under all possible situations of sampling data as well as approaches to parameter and goodness-of-fit testing. Describes the Statistical Methods Concepts Theories and Applications of This DistributionCompiling findings from dozens of scientific journals and hundreds of research papers the author first gives a careful and thorough mathematical description of the Weibull distribution and all of its features. He then deals with Weibull analysis using classical and Bayesian approaches along with graphical and linear maximum likelihood techniques to estimate the three Weibull parameters. The author also explores the inference of Weibull processes Weibull parameter testing and different types of goodness-of-fit tests and methods. Successfully Apply the Weibull ModelBy using inferential procedures for estimating testing forecasting and simulating data this self-contained detailed handbook shows how to solve statistical life science and engineering problems. | The Weibull Distribution A Handbook

GBP 44.99
1

Component-Based Software Engineering Methods and Metrics

Component-Based Software Engineering Methods and Metrics

This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories characteristics repository interaction complexity and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues (2) interaction and integration issues and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability interaction and integration complexities and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers academicians and organizations in the context of CBSE but also suggests innovative efficient and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students research scholars software developers and software designers or individuals interested in software engineering especially in component-based software engineering can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences. | Component-Based Software Engineering Methods and Metrics

GBP 105.00
1

Statistical Inference Based on Divergence Measures

Statistical Inference Based on Divergence Measures

The idea of using functionals of Information Theory such as entropies or divergences in statistical inference is not new. However in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures explores classical problems of statistical inference such as estimation and hypothesis testing on the basis of measures of entropy and divergence. The first two chapters form an overview from a statistical perspective of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald Rao and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions. Clear comprehensive and logically developed this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems but the tools to put it into practice.

GBP 44.99
1

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

GBP 84.99
1

Data Analysis Using Hierarchical Generalized Linear Models with R

Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown and various practical analytical tools have been developed. This book summarizes developments within the field and using data examples illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects survival analysis and frailty models multivariate HGLMs factor and structural equation models robust modelling of random effects models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students who wish to perform data modelling beyond the frequentist framework and especially for those searching for a bridge between Bayesian and frequentist statistics. | Data Analysis Using Hierarchical Generalized Linear Models with R

GBP 44.99
1

Accelerated Life Models Modeling and Statistical Analysis

Accelerated Life Models Modeling and Statistical Analysis

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified systematic presentation this monograph fully details those models and explores areas of accelerated life testing usually only touched upon in the literature. Accelerated Life Models: Modeling and Statistical Analysis presents models methods of data collection and statistical analysis for failure-time regression data in accelerated life testing and for degradation data with explanatory variables. In addition to the classical results the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. They also examine the simultaneous analysis of degradation and failure-time data when the intensities of failure in different modes depend on the level of degradation and the values of explanatory variables. The authors avoid technical details by explaining the ideas and referring to resources where thorough analysis can be found. Whether used for teaching research or general reference Accelerated Life Models: Modeling and Statistical Analysis provides new and known models and modern methods of accelerated life data analysis. | Accelerated Life Models Modeling and Statistical Analysis

GBP 59.99
1

Theoretical Statistics

Theoretical Statistics

A text that stresses the general concepts of the theory of statistics Theoretical Statistics provides a systematic statement of the theory of statistics emphasizing general concepts rather than mathematical rigor. Chapters 1 through 3 provide an overview of statistics and discuss some of the basic philosophical ideas and problems behind statistical procedures. Chapters 4 and 5 cover hypothesis testing with simple and null hypotheses respectively. Subsequent chapters discuss non-parametrics interval estimation point estimation asymptotics Bayesian procedure and deviation theory. Student familiarity with standard statistical techniques is assumed.

GBP 180.00
1

A Course in the Large Sample Theory of Statistical Inference

A Course in the Large Sample Theory of Statistical Inference

This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis including situations where parameters are estimated from the complete ungrouped data. This book is based on lecture notes prepared by the first author subsequently edited expanded and updated by the second author. Key features: Succinct account of the concept of “asymptotic linearity” and its uses Simplified derivations of the major results under an assumption of joint asymptotic normality Inclusion of numerical illustrations practical examples and advice Highlighting some unexpected consequences of the theory Large number of exercises many with hints to solutions Some facility with linear algebra and with real analysis including ‘epsilon-delta’ arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary and experience with applying these concepts to data analysis would be very helpful. | A Course in the Large Sample Theory of Statistical Inference

GBP 84.99
1

Applied Stochastic Modelling

Applied Stochastic Modelling

Highlighting modern computational methods Applied Stochastic Modelling Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures this edition offers numerous updates throughout. New to the Second Edition An extended discussion on Bayesian methods A large number of new exercises A new appendix on computational methods The book covers both contemporary and classical aspects of statistics including survival analysis Kernel density estimation Markov chain Monte Carlo hypothesis testing regression bootstrap and generalised linear models. Although the book can be used without reference to computational programs the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB and R programs found in the text as well as lecture slides and other ancillary material are available for download at www. crcpress. com Continuing in the bestselling tradition of its predecessor this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

GBP 175.00
1

Handbook of Statistical Methods for Randomized Controlled Trials

Handbook of Statistical Methods for Randomized Controlled Trials

Statistical concepts provide scientific framework in experimental studies including randomized controlled trials. In order to design monitor analyze and draw conclusions scientifically from such clinical trials clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing subgroup analysis competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials analysis of safety outcomes non-inferiority trials incorporating historical data and validation of surrogate outcomes.

GBP 59.99
1

Statistical Design and Analysis of Stability Studies

Statistical Design and Analysis of Stability Studies

The US Food and Drug Administration's Report to the Nation in 2004 and 2005 indicated that one of the top reasons for drug recall was that stability data did not support existing expiration dates. Pharmaceutical companies conduct stability studies to characterize the degradation of drug products and to estimate drug shelf life. Illustrating how stability studies play an important role in drug safety and quality assurance Statistical Design and Analysis of Stability Studies presents the principles and methodologies in the design and analysis of stability studies. After introducing the basic concepts of stability testing the book focuses on short-term stability studies and reviews several methods for estimating drug expiration dating periods. It then compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. Following a chapter on the statistical methods for stability analysis under a linear mixed effects model the book examines stability analyses with discrete responses multiple components and frozen drug products. In addition the author provides statistical methods for dissolution testing and explores current issues and recent developments in stability studies. To ensure the safety of consumers professionals in the field must carry out stability studies to determine the reliability of drug products during their expiration period. This book provides the material necessary for you to perform stability designs and analyses in pharmaceutical research and development.

GBP 44.99
1

Fundamentals of Internet of Things

Fundamentals of Internet of Things

The Internet of Things (IoT) networks have revolutionized the world and have innumerable real-time applications on automation. A few examples include driverless cars remote monitoring of the elderly remote order of tea or coffee of your choice from a vending machine and home/industrial automation amongst others. Fundamentals of Internet of Things build the foundations of IoT networks by leveraging the relevant concepts from signal processing communications net-works and machine learning. The book covers two fundamental components of IoT networks namely the Internet and Things. In particular the book focuses on networking concepts protocols clustering data fusion localization energy harvesting control optimization data analytics fog computing privacy and security including elliptic curve cryptography and blockchain technology. Most of the existing books are theoretical and without many mathematical details and examples. In addition some essential topics of the IoT networks are also missing in the existing books. Features: • The book covers cutting-edge research topics• Provides mathematical understanding of the topics in addition to relevant theory and insights• Includes illustrations with hand-solved numerical examples for visualization of the theory and testing of understanding• Lucid and crisp explanation to lessen the study time of the reader The book is a complete package of the fundamentals of IoT networks and is suitable for graduate-level students and researchers who want to dive into the world of IoT networks.

GBP 145.00
1

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry academia and government. It introduces the tidyverse suite of R packages including the ggplot2 package for data visualization and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses the book covers traditional introductory statistics topics like confidence intervals hypothesis testing and multiple regression modeling while focusing on visualization throughout. Features: ● Assumes minimal prerequisites notably no prior calculus nor coding experience ● Motivates theory using real-world data including all domestic flights leaving New York City in 2013 the Gapminder project and the data journalism website FiveThirtyEight. com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive. com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics at both the undergraduate and graduate levels.

GBP 66.99
1

Solvency Models Assessment and Regulation

Solvency Models Assessment and Regulation

Until now there were no published analyses of the recent solvency work conducted in Europe specifically the risk categories proposed by the International Actuarial Association (IAA). Answering the insurance industry's demand in the wake of the EU Solvency II project Solvency: Models Assessment and Regulation provides a concrete summary and review of solvency and inspires additional work in the field. Following an introduction to the concept the first section of the book provides a historical review of solvency detailing solvency regulation and accounting within the EU. A review of the steps leading to Solvency II looks at accounting supervision the actuarial field the first phase of Solvency II international approaches to banking and the solvency systems of 12 major nations. The second section explores the current basis for solvency modeling focusing on the valuation of assets and liabilities dependency and various conservative approaches as well as a baseline and benchmark approach. This section also provides examples of risk structure and the effects of diversification. The final section discusses groups and internal modeling as it relates to EU Solvency II. It addresses insurance groups financial conglomerates reinsurance the importance of internal modeling and stress testing and the current state of the second phase of EU Solvency II. | Solvency Models Assessment and Regulation

GBP 59.99
1

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2 provides the reader with the concepts and tools necessary to bring a robot to life through programming. It will equip the reader with the skills necessary to undertake projects with ROS2 the new version of ROS. It is not necessary to have previous experience with ROS2 as it will describe its concepts tools and methodologies from the beginning. Key Features Uses the two programming languages officially supported in ROS2 (C++ mainly and Python) Approaches ROS2 from three different but complementary dimensions: the Community Computation Graph and the Workspace Includes a complete simulated robot development and testing strategies Behavior Trees and Nav2 description setup and use A GitHub repository with code to assist readers It will appeal to motivated engineering students engineers and professionals working with robot programming.

GBP 44.99
1

Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
1

Python Packages

Python Packages

Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this the text builds up to advanced topics such as automated testing creating documentation versioning and updating a package and implementing continuous integration and deployment. Covering the entire Python packaging life cycle this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry cookiecutter pytest sphinx GitHub and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on automatically tested and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package but also how to document it test it publish it to the Python Package Index (PyPI) and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

GBP 35.99
1

Machine Learning for Factor Investing: R Version

Machine Learning for Factor Investing: R Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material along with the content of the book is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

GBP 66.99
1

Exercises and Solutions in Biostatistical Theory

Exercises and Solutions in Biostatistical Theory

Drawn from nearly four decades of Lawrence L. Kupper‘s teaching experiences as a distinguished professor in the Department of Biostatistics at the University of North Carolina Exercises and Solutions in Biostatistical Theory presents theoretical statistical concepts numerous exercises and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical biostatistical principles to real-world situations including some of the authors own biostatistical work that has addressed complicated design and analysis issues in the health sciences. This classroom-tested material is arranged sequentially starting with a chapter on basic probability theory followed by chapters on univariate distribution theory and multivariate distribution theory. The last two chapters on statistical inference cover estimation theory and hypothesis testing theory. Each chapter begins with an in-depth introduction that summarizes the biostatistical principles needed to help solve the exercises. Exercises range in level of difficulty from fairly basic to more challenging (identified with asterisks). By working through the exercises and detailed solutions in this book students will develop a deep understanding of the principles of biostatistical theory. The text shows how the biostatistical theory is effectively used to address important biostatistical issues in a variety of real-world settings. Mastering the theoretical biostatistical principles described in the book will prepare students for successful study of higher-level statistical theory and will help them become better biostatisticians.

GBP 175.00
1