Introductory Statistical Inference with the Likelihood Function

1. Front Matter

   Pages i-xvi

   PDF

2. Introduction

   • Charles A. Rohde

   Pages 1-10

3. The Statistical Approach

   • Charles A. Rohde

   Pages 11-16

4. Estimation

   • Charles A. Rohde

   Pages 17-26

5. Interval Estimation

   • Charles A. Rohde

   Pages 27-40

6. Hypothesis Testing

   • Charles A. Rohde

   Pages 41-62

7. Standard Practice of Statistics

   • Charles A. Rohde

   Pages 63-65

8. Maximum Likelihood: Basic Results

   • Charles A. Rohde

   Pages 67-83

9. Linear Models

   • Charles A. Rohde

   Pages 85-100

10. Other Estimation Methods

    • Charles A. Rohde

    Pages 101-124

11. Decision Theory

    • Charles A. Rohde

    Pages 125-132

12. Sufficiency

    • Charles A. Rohde

    Pages 133-146

13. Conditionality

    • Charles A. Rohde

    Pages 147-150

14. Statistical Principles

    • Charles A. Rohde

    Pages 151-165

15. Bayesian Inference

    • Charles A. Rohde

    Pages 167-180

16. Bayesian Statistics: Computation

    • Charles A. Rohde

    Pages 181-185

17. Bayesian Inference: Miscellaneous

    • Charles A. Rohde

    Pages 187-195

18. Pure Likelihood Methods

    • Charles A. Rohde

    Pages 197-209

19. Pure Likelihood Methods and Nuisance Parameters

    • Charles A. Rohde

    Pages 211-235

20. Other Inference Methods and Concepts

    • Charles A. Rohde

    Pages 237-248

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.

• Bayes' Theorem Biostatistics
• Bayesian Inference
• Biostatistics Methodology
• Likelihood Function
• Likelihood Modeling
• Pure Likelihood Inference
• Severity
• Statistical Inference for Biology

• Bloomberg School of Health, Johns Hopkins University, Baltimore, USA

  Charles A. Rohde

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.

Policies and ethics