Exercises and solutions in biostatistical theory. (©2011)

• Record Type: Book
• Further Information: Note: Lawrence L. Kupper, Sean M. O'Brien, Brian H. Neelon.
• Other Names: Kupper, Lawrence L; O'Brien, Sean M; Neelon, Brian H
• Contents: Basic Probability Theory ; Counting Formulas (N-tuples, permutations, combinations, Pascal’s identity, Vandermonde’s identity); Probability Formulas (union, intersection, complement, mutually exclusive events, conditional probability, independence, partitions, Bayes’ theorem) Univariate Distribution Theory ; Discrete and Continuous Random Variables; Cumulative Distribution Functions; Median and Mode; Expectation Theory; Some Important Expectations (mean, variance, moments, moment generating function, probability generating function); Inequalities Involving Expectations; Some Important Probability Distributions for Discrete Random Variables; Some Important Distributions (i.e., Density Functions) for Continuous Random Variables Multivariate Distribution Theory ; Discrete and Continuous Multivariate Distributions; Multivariate Cumulative Distribution Functions; Expectation Theory (covariance, correlation, moment generating function); Marginal Distributions; Conditional Distributions and Expectations; Mutual Independence among a Set of Random Variables; Random Sample; Some Important Multivariate Discrete and Continuous Probability Distributions; Special Topics of Interest (mean and variance of a linear function, convergence in distribution and the Central Limit Theorem, order statistics, transformations) Estimation Theory ; Point Estimation of Population Parameters (method of moments, unweighted and weighted least squares, maximum likelihood); Data Reduction and Joint Sufficiency (Factorization Theorem); Methods for Evaluating the Properties of a Point Estimator (mean-squared error, Cramér–Rao lower bound, efficiency, completeness, Rao–Blackwell theorem); Interval Estimation of Population Parameters (normal distribution-based exact intervals, Slutsky’s theorem, consistency, maximum-likelihood-based approximate intervals) Hypothesis Testing Theory ; Basic Principles (simple and composite hypotheses, null and alternative hypotheses, Type I and Type II errors, power, P-value); Most Powerful (MP) and Uniformly Most Powerful (UMP) Tests (Neyman–Pearson Lemma); Large-Sample ML-Based Methods for Testing a Simple Null Hypothesis versus a Composite Alternative Hypothesis (likelihood ratio, Wald, and score tests); Large-Sample ML-Based Methods for Testing a Composite Null Hypothesis versus a Composite Alternative Hypothesis (likelihood ratio, Wald, and score tests) Appendix: Useful Mathematical Results References Index Exercises and Solutions appear at the end of each chapter.
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2011
• Copyright Date: 2011
• Extent: 1 online resource (xvii, 402 pages)
• Subjects: 570.15195; Biometry -- Problems, exercises, etc; Biometry; Electronic books; Problems and exercises
• Languages: English
• ISBNs: 9781439895023; 1439895023
• Notes: Note: Includes bibliographical references and index.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.