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Test Configurations, Stabilities and Canonical Kahler Metrics - Toshiki Mabuchi - Bog - Springer Verlag, Singapore - Plusbog.dk

Diagnostic Methods in Time Series - Anna Clara Monti - Bog - Springer Verlag, Singapore - Plusbog.dk

Diagnostic Methods in Time Series - Anna Clara Monti - Bog - Springer Verlag, Singapore - Plusbog.dk

This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics.

DKK 519.00
3

Recycled Polyester - - Bog - Springer Verlag, Singapore - Plusbog.dk

Asymptotic Statistical Inference - Shailaja Deshmukh - Bog - Springer Verlag, Singapore - Plusbog.dk

Asymptotic Statistical Inference - Shailaja Deshmukh - Bog - Springer Verlag, Singapore - Plusbog.dk

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald''s test, their relationship with the likelihood ratio test and Karl Pearson''s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson''s chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.

DKK 646.00
1

Iberian World Empires and the Globalization of Europe 1415–1668 - Bartolome Yun Casalilla - Bog - Springer Verlag, Singapore - Plusbog.dk

Multiple Comparisons for Bernoulli Data - Taka Aki Shiraishi - Bog - Springer Verlag, Singapore - Plusbog.dk

Automation of Trading Machine for Traders - Jacinta Chan - Bog - Springer Verlag, Singapore - Plusbog.dk

Fundamentals of Electronic Devices and Circuits - G.s. Tomar - Bog - Springer Verlag, Singapore - Plusbog.dk

The Authoritative Guide on Harbor - Haining Zhang - Bog - Springer Verlag, Singapore - Plusbog.dk

Li-S and Li-O2 Batteries with High Specific Energy - Hongzhang Zhang - Bog - Springer Verlag, Singapore - Plusbog.dk

Characterizing Interdependencies of Multiple Time Series - Kosuke Oya - Bog - Springer Verlag, Singapore - Plusbog.dk

Characterizing Interdependencies of Multiple Time Series - Kosuke Oya - Bog - Springer Verlag, Singapore - Plusbog.dk

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an improved version of the basic concepts for measuring the one-way effect, reciprocity, and association of multiple time series, which were originally proposed by Hosoya. Then the statistical inferences of these measures are presented, with a focus on the stationary multivariate autoregressive moving-average processes, which include the estimation and test of causality change. Empirical analyses are provided to illustrate what alternative aspects are detected and how the methods introduced here can be conveniently applied. Most of the materials in Chapters 4 and 5 are based on the authors'' latest research work. Subsidiary items are collected in the Appendix.

DKK 468.00
1

Governing Corporate Tax Management - Rajah Rasiah - Bog - Springer Verlag, Singapore - Plusbog.dk