Introduction to Mathematical StatisticsPearson Education, 2005 - 704 páginas This classic book retains its outstanding ongoing features and continues to provide readers with excellent background material necessary for a successful understanding of mathematical statistics.Chapter topics cover classical statistical inference procedures in estimation and testing, and an in-depth treatment of sufficiency and testing theory—including uniformly most powerful tests and likelihood ratios. Many illustrative examples and exercises enhance the presentation of material throughout the book.For a more complete understanding of mathematical statistics. |
Índice
Multivariate Distributions | 73 |
Some Special Distributions | 133 |
Unbiasedness Consistency and Limiting Distributions | 197 |
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Introduction to Mathematical Statistics Robert V. Hogg,Joseph W. McKean,Allen Thornton Craig Vista de fragmentos - 2005 |
Introduction to Mathematical Statistics Robert V. Hogg,Joseph W. McKean,Allen Thornton Craig No hay ninguna vista previa disponible - 2005 |
Introduction to Mathematical Statistics Hogg Robert V.,Hogg No hay ninguna vista previa disponible - 2005 |
Términos y frases comunes
algorithm approximate assume asymptotic Bayes binomial bootstrap C₁ chi-square distribution complete sufficient statistic compute conditional pdf confidence interval Consider continuous random variable continuous type converges critical region defined degrees of freedom denote a random determine discrete discussed distribution with parameters equal equation Example Exercise expression Find Fx(x gamma distribution given H₁ Hence independent random variables inequality integral joint pdf Let X1 likelihood function likelihood ratio test linear LS estimator matrix median MVUE normal distribution null observations obtain order statistics p-value pdf f(x point estimate Poisson distribution power function Proof quantiles random sample random vector reject respectively result S-PLUS sample mean sample space sequence Show significance level sufficient statistic Suppose t-distribution test statistic Theorem unbiased estimator variance X₁ X1 and X2 Xn denote Y₁ zero elsewhere