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Informationen zum Autor Cohen\, Klappentext Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified formats for estimates, graphical techniques, and numerous tables and charts allowing users to calculate estimates and analyze sample data quickly and easily. Anno Zusammenfassung Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified formats for estimates, graphical techniques, and numerous tables and charts allowing users to calculate estimates and analyze sample data quickly and easily. Anno Inhaltsverzeichnis Contents Preface List of Illustrations List of Tables List of Programs 1.INTRODUCTION. Introductory Remarks. An Overview of Skewed Distributions. Parameter Estimation. Some Comparisons.2. CONCEPTS FOR THE ANALYSIS OF RELIABILITY AND LIFE DISTRIBUTION DATA. Life Distributions and Reliability Discrete Models. 3.THE WEIBULL, EXPONENTIAL, AND EXTREME VALUE DISTRIBUTIONS. Background. Characteristics of the Weibull Distribution. Maximum Likelihood Estimation. Moment Estimators. Modified Moment Estimators. Wycoff, Bain, Engelhardt, and Zanakis Estimators. Special Cases: Shape Parameter o Known. Errors of Estimates. The Exponential Distribution. The Extreme Value Distribution. Illustrative Examples. Reflections. 4. LOGNORMAL DISTRIBUTION Introduction. Some Fundamentals. Moment Estimators. Maximum Likelihood Estimators. Asymptotic Variances and Covariances. Modified Moment Estimators. Illustrative Examples. Reflections. 5. THE INVERSE GAUSSIAN DISTRIBUTION. Background. The Probability Density Function. Maximum Likelihood Estimation. Asymptotic Variances and Covariances. Moment Estimators. Modified Moment Estimators. Illustrative Examples. Reflections. 6. THE GAMMA DISTRIBUTION. Background. The Density Function and Its Characteristics.Moment Estimators. Maximum Likelihood Estimators. Asymptotic Variances and Covariances. Modified Moment Estimators. Illustrative Examples. Reflections. 7. CENSORED SAMPLING IN THE EXPONENTIAL AND WEIBULL DISTRIBUTIONS. Introduction. Progressively Censored Samples. Censored Samples from the Exponential Distribution. Censored Samples from the Weibull Distribution. The Hazard Plot. Estimate Variances and Covariances. An Illustrative Example. 8. CENSORED AND TRUNCATED SAMPLES FROM THENORMAL AND THE LOGNORMAL DISTRIBUTIONS. Introductory Remarks. Maximum Likelihood Estimation Modified Maximum Likelihood Estimators for Lognormal Parameters Based on Censored Samples. Maximum Likelihood Estimation in the Normal Distribution. An Illustrative Example from a Lognormal Distribution. CENSORED SAMPLING IN THE INVERSE GAUSSIAN AND GAMMA DISTRIBUTIONS. Introduction. Censored Sampling in the Inverse Gaussian Distribution. A Pseudocomplete Sample Technique. Censored Sampling in the Gamma Distribution. An Illustrative Example. 10. THE RAYLEIGH DISTRIBUTION. Introduction. Maximum Likelihood and Moment Estimation in the p-Dimensional Distribution. Special Cases. Complete Sample Estimators When p = 1, 2, and 3. Two-Parameter Rayleigh Distribution. Estimation in the Two-Parameter Rayleigh Distribution. Truncated Samples. Censored Samples. Reliability of Estimates. Illustrative Examples. Parameter Estimation in the Two-Parameter Rayleigh Distribution of Dimension 2, When Samples Are Censored. Some Concluding Remarks 11. THE PARETO DISTRIBUTION. Introduction. Some Fundamentals. Parameter Estimation from Complete Samples. Parameter Estimation from Truncated Censored Samples. Reliability of Estimates. An Illustrative Example 12. THE GENERALIZED GAMMA DISTRIBUTION. Introduction. Parameter Estimation in the Three-Parameter Distribution. The Four-Parameter Distribution. Estimate Variances and Covariances. Simplified Computational Procedures for the Modified Moment Estimators. Illustrative Examples Censored Samples. Asymptotic Var...
