This authoritative new volume treats a wide class of distributions that constitute plausible alternatives to normality -- such as short- and long-tailed symmetric distributions and moderately skewed distributions -- all having finite mean and variance. Robust Inference illustrates the appropriateness of various robust methods for solving both one-sample and multisample statistical inference problems ... develops Laguerre series expansions for Student's t and variance-ratio F statistic distributions ... analyzes normal and nonnormal distribution efficiencies ... works out modified maximum likelihood (MML) estimators based on type II censored samples for log-normal, logistic, exponential, and Rayleigh distributions ... uses MML estimators in constructing robust hypothesis-testing procedures ... considers the specialized topics of regression, analysis of variance, classification, and sample survey ... discusses goodness-of-fit tests ... describes Q-Q plots in a special appendix ... and much more. An outstanding, time-saving reference for theoreticians and practitioners of statistics, Robust Inference is also an excellent auxiliary text for an undergraduate- or graduate-level course on robustness. Book jacket.
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ESTIMATION PROCEDURES FOR CENSORED SAMPLES
MMLEs FOR OTHER DISTRIBUTIONS
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approximate assumed assumption of normality asymptotic power function Balakrishnan bias Biometrika bution calculated Chapter chi-square compute D'Agostino data of Example degrees of freedom distri efficient esti expected values exponential distribution F distribution F test finite given in Table Huber I I censored samples largest observations likelihood function linear location and scale log-normal logistic distribution long-tailed distributions mators maximum likelihood method ML equations MML estimators MML10 estimators MMLE nonnormal normal distribution Note null distribution numerous obtained order statistics outliers Pearson percentage points population random sample regression rejection respectively robust estimators robust procedures sample mean scale parameters significance level simulated values skew distributions standard error Student's t distribution sum of squares symmetric distributions tc test Technometrics tests based Theorem Tiku truncated truncated distribution type I error type I I type II censored unbiased estimator underlying distribution variances and covariances