7 edition of **Parametric statistical inference** found in the catalog.

- 292 Want to read
- 13 Currently reading

Published
**1996**
by Clarendon Press in Oxford
.

Written in English

- Mathematical statistics.

**Edition Notes**

Includes bibliographical references (p. [449]-467) and index.

Statement | J.K. Lindsey. |

Classifications | |
---|---|

LC Classifications | QA276 .L547 1996 |

The Physical Object | |

Pagination | xviii, 490 p. : |

Number of Pages | 490 |

ID Numbers | |

Open Library | OL721998M |

ISBN 10 | 0198523599 |

LC Control Number | 97109161 |

OCLC/WorldCa | 35017336 |

Parametric Statistical Inference. Basic Theory and Modern Approaches | Shelemyahu Zacks, V. Lakshmikantham and C. P. Tsokos (Auth.) | download | B–OK. Download. In this chapter, we get a glimpse of how to use the principles of the previous chapter in the context of parametric inference. We have already discussed the problem of parametric inference in the first chapter (the Bayesian estimate of the probability that the Sun will rise tomorrow), and here I .

Discusses fundamental topics common to many fields of statistical inference, and which offer a point of departure for in-depth study About this Textbook This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students. Download Statistical Inference In Science ebook PDF or Read Online books in PDF, EPUB, Parametric Statistical Inference. Author: James K. Lindsey ISBN: Genre: Mathematics These are here brought together for the firsttime as the central themes of a book on statistical inference. This book is appropriate as an advanced.

Statistical inference. Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.

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Pocket Book of Statistical Tables, compiled by R. E Odeh, D B. Owen, Z. Bimbaum, and L Fisher The Information in Contingency Tables, D V Gokhale and Solomon Kullback Statistical Analysis of Reliability and Life-Testing Models: Theory and Methods, Lee J.

Bain Elementary Statistical Quality Control, Irving W Burr The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters.

Raghunath Arnab, in Survey Sampling Theory and Applications, Introduction. Likelihood is the most important tool for parametric inference whereas empirical likelihood (EL) is a powerful nonparametric approach to statistical inference.

EL was first introduced in survey sampling by Hartley Parametric statistical inference book Rao () in the name of scale load approach. The modern concept of EL was introduced by. This publication examines the distinct philosophical foundations of different statistical modes of parametric inference.

Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. This book discusses examples in parametric inference with R.

Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background.

This book discusses the fitting of parametric statistical models to data samples. Emphasis is placed on: (i) how to recognize situations where the problem is non-standard when parameter estimates behave unusually, and (ii) the use of parametric bootstrap resampling methods in analyzing such problems.A frequentist likelihood-based viewpoint is adopted, for which there is a well-established and.

Parametric statistical inference by Lindsey, J. (James K.) Publication date Topics Mathematical statistics Publisher Oxford: Clarendon Press Borrow this book to access EPUB and PDF files. IN COLLECTIONS. Books to Borrow. Books for People with Print Disabilities. Trent University Library : What Is Nonparametric Inference.

The basic idea of nonparametric inference is to use data to infer an unknown quantity while making as few assumptions as possible. Usually, this means using statistical models that are inﬁnite-dimensional.

Indeed, a better name for nonparametric inference might be inﬁnite-dimensional inference. But it is. Purchase Parametric Statistical Inference - 1st Edition. Print Book & E-Book. ISBNBook Edition: 1. Inference involves drawing conclusions about some general phenomenon from limited empirical observations in the face of random variability.

In a scientific context, the general must include the completely unforeseen if all possibilities are to be considered. Many of the statistical models most used to describe such phenomena belong to one of a small number of families--the exponential.

Parametric statistical inference arises when we have information for the model describing an uncertain experiment sans a few values, called parameters, of the model.

If the parameter values are known, we have problems more of the probabilistic kind than statistical ones. The parameter values need to be obtained based on some data.

This book discusses the fitting of parametric statistical models to data samples. Emphasis is placed on (i) how to recognize situations where the problem is non-standard, when parameter estimates behave unusually, and (ii) the use of parametric bootstrap resampling methods in analysing such problems.

Simple and practical model building is an underlying : Russell Cheng. Nonparametric statistical inference is a collective term given to inferences thatare valid under less restrictive assumptions than with classical (parametric)statistical inference.

The assumptions that can be relaxed include specifying theprobability distribution of the population from which the sample was drawn andthe level of measurement. A History of Parametric Statistical Inference from Bernoulli to Fisher, book.

Read 2 reviews from the world's largest community for readers. Th /5. I strongly recommend the book for any student or researcher of "Statistical Inference" and also to all * Zentrallblat fur Mathematik, vol.* Read more User-contributed reviews.

Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels.

The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools. Bootstrapping: A Nonparametric Approach to Statistical Inference, by C. Mooney and R.

Duval, provides one of the best introductions to the bootstrap you are likely to encounter. Although it was written for social science researchers, anyone familiar with classical statistical procedures will.

The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate. Statistical parametric mapping is a voxel-based approach, employing classical inference, to make some comment about regionally specific responses to experimental factors.

In order to assign an observed response to a particular brain structure, or cortical area, the data must conform to. statistical inference 3 12 Properties of Maximum Likelihood Estimates 71 13 Hypothesis Testing: General Framework 79 14 The Wald test and t-test 86 15 P-values 90 16 The Permutation Test 95 17 The Likelihood Ratio Test 98 18 Testing Mendel’s Theory 19 Multiple Testing 20 Regression Function and General Regression Model 21 Scatter Plots and Simple Linear Regression Model File Size: 6MB.

Parametric statistics is a branch of statistics which assumes that sample data come from a population that can be adequately modeled by a probability distribution that has a fixed set of parameters.

Conversely a non-parametric model differs precisely in that the parameter set (or feature set in machine learning) is not fixed and can increase, or even decrease, if new relevant information is.Buy Parametric Statistical Inference Books online at best prices in India by J K Lindsey,James K Lindsey,J.

K. (Professor of Biostatistics, Limburgs University, and Professor of Quantitative Methodology, University of Liege) Lindsey from Buy Parametric Statistical Inference online of India’s Largest Online Book Store, Only Genuine Products.Principles of Statistical Inference In this important book, D.

R. Cox develops the key concepts of the theory of statistical inference, in particular describing and comparing the main ideas and controversies over foundational issues that have rumbled on for more than years.

Continuing a year career of contribution to statistical thought.