From finite sample to asymptotic methods in statistics / Pranab K. Sen, Julio M. Singer, Antonio C. Pedroso de Lima.

By:

Sen, Pranab Kumar, 1937-

Contributor(s):

Material type: Text

TextSeries: Cambridge series on statistical and probabilistic mathematicsPublication details: Cambridge ; New York : Cambridge University Press, ©2010.Description: 1 online resource (xii, 386 pages) : illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

0511639457
9780511639456
9780511641893
0511641893
9780511806957
0511806957
1107210836
9781107210837
0511638388
9780511638381
0511640536
9780511640537

Subject(s): Genre/Form:

Additional physical formats: Print version:: From finite sample to asymptotic methods in statistics.DDC classification:

519.5 22

LOC classification:

QA276 .S358 2010eb

Online resources:

Click here to access online

Contents:

Motivation and basic tools -- Estimation theory -- Hypothesis testing -- Elements of statistical decision theory -- Stochastic processes: an overview -- Stochastic convergence and probability inequalities -- Asymptotic distributions -- Asymptotic behavior of estimators and tests -- Categorical data models -- Regression models -- Weak convergence and Gaussian processes.

Summary: "Exact statistical inference may be employed in diverse fields of science and technology. As problems become more complex and sample sizes become larger, mathematical and computational difficulties can arise that require the use of approximate statistical methods. Such methods are justified by asymptotic arguments but are still based on the concepts and principles that underlie exact statistical inference. With this in perspective, this book presents a broad view of exact statistical inference and the development of asymptotic statistical inference, providing a justification for the use of asymptotic methods for large samples. Methodological results are developed on a concrete and yet rigorous mathematical level and are applied to a variety of problems that include categorical data, regression, and survival analyses. This book is designed as a textbook for advanced undergraduate or beginning graduate students in statistics, biostatistics, or applied statistics but may also be used as a reference for academic researchers"--Provided by publisher.

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"Exact statistical inference may be employed in diverse fields of science and technology. As problems become more complex and sample sizes become larger, mathematical and computational difficulties can arise that require the use of approximate statistical methods. Such methods are justified by asymptotic arguments but are still based on the concepts and principles that underlie exact statistical inference. With this in perspective, this book presents a broad view of exact statistical inference and the development of asymptotic statistical inference, providing a justification for the use of asymptotic methods for large samples. Methodological results are developed on a concrete and yet rigorous mathematical level and are applied to a variety of problems that include categorical data, regression, and survival analyses. This book is designed as a textbook for advanced undergraduate or beginning graduate students in statistics, biostatistics, or applied statistics but may also be used as a reference for academic researchers"--Provided by publisher.