Name: Asymptotic Statistical Inference: A Basic Course Using r
SKU: 53532739
Price: 105.63 EUR
Availability: InStock
Author: Shailaja Deshmukh

portada Asymptotic Statistical Inference: A Basic Course Using r

Asymptotic Statistical Inference: A Basic Course Using r

Type

Physical Book

Author

Shailaja Deshmukh

Publisher

Springer

Year

2021

Language

English

Pages

340

Format

Hardcover

ISBN13

9789811590023

Edition No.

Synopsis "Asymptotic Statistical Inference: A Basic Course Using r "

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.

Shailaja Deshmukh

(Author)

View Author's Page

Shailaja Rajendra Deshmukh es profesora de estadística en la Universidad de Pune, India. Su especialidad incluye procesos estocásticos, probabilidad aplicada y análisis de datos de microarrays. Ha publicado varios libros que combinan teoría estadística con aplicaciones prácticas: Multiple Decrement Models in Insurance: An Introduction Using R (2012), Actuarial Statistics: An Introduction Using R, Statistics Using R, y Microarray Data: Statistical Analysis Using R. Su obra suele incorporar ejemplos reales, modelos estadísticos y uso del software R para hacer accesible la cuantificación de riesgos, primas o reservas en seguros, entre otras aplicaciones.

Su influencia se observa en el uso educativo de sus libros, en cursos de estadística y actuaría, y en sus publicaciones en revistas revisadas por pares, lo que la sitúa como una figura valorada en su campo.

See less