Read more
This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. This book is about some of the basic principles of statistics that are necessary to understand and evaluate methods for analyzing complex data sets. The likelihood function is used for pure likelihood inference throughout the book. There is also coverage of severity and finite population sampling. The material was developed from an introductory statistical theory course taught by the author at the Johns Hopkins University's Department of Biostatistics. Students and instructors in public health programs will benefit from the likelihood modeling approach that is used throughout the text. This will also appeal to epidemiologists and psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes with sections on Bayesian computation and inference. An appendix contains unique coverage of the interpretation of probability, and coverage of probability and mathematical concepts.
List of contents
Contents.- Preface.- Introduction.- The Statistical Approach.- Estimation.- Interval Estimation.- Hypothesis Testing.- Maximum Likelihood: Basic Results.- Linear Model.- Other Estimation Methods.- Decision Theory.- Sufficiency.- Conditionality.- Statistical Principles.- The Practice of Statistics.- Bayesian Statistics: Philosophy and Theory.- Priors.- Bayesian Statistics: Computation.- Bayesian Inference: Miscellaneous.- Sufficiency.- A Probability and Mathematical Concepts.
About the author
Charles A. Rohde received his PhD at N.C. State in 1964 and has been at Johns Hopkins since then. He served as Department Chair for the Department of Biostatistics from 1981 to 1996. Professor Rohde's areas of research have included generalized inverses of matrices, linear models and pure likelihood methods.
Summary
This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. This book is about some of the basic principles of statistics that are necessary to understand and evaluate methods for analyzing complex data sets. The likelihood function is used for pure likelihood inference throughout the book. There is also coverage of severity and finite population sampling. The material was developed from an introductory statistical theory course taught by the author at the Johns Hopkins University’s Department of Biostatistics. Students and instructors in public health programs will benefit from the likelihood modeling approach that is used throughout the text. This will also appeal to epidemiologists and psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes with sections on Bayesian computation and inference. An appendix contains unique coverage of the interpretation of probability, and coverage of probability and mathematical concepts.
