ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477859

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

This article proposes two estimators of the correlation coefficient, ρ, when statisticians will not construct a master file on individuals because of confidentiality issues. The approach depends on grouping the data according to the values of one of the variables. Group means and total variance for both variables are required to calculate the estimators. The complete bivariate sample need never be compiled. The estimators are shown to be asymptotically normal. Asymptotic and Monte Carlo results are examined. For 1,000 observations in 10 groups, these results indicate that the ratio of the mean squared error (MSE) for one of the proposed estimators relative to that for the usual estimator of ρ ranges from .82 to .95 as ρ ranges from .9 to .25. The proposed estimators are more efficient than previous estimators for grouped data.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477860

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

Many times, aspects of populations exist that must satisfy a stochastic ordering requirement. Nevertheless, estimates may not bear out this stochastic ordering because of the inherent variability of the observations. This article will consider the problem of finding maximum likelihood estimates of stochastically ordered survival functions for the cases (a) one survival function being fixed in advance and (b) estimating two survival functions when the data include censored observations. A numerical example is discussed in detail to illustrate the solution to this problem.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477861

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

The behavior of linear combinations of order statistics (L-statistics) under jackknifing is discussed. The asymptotic behavior of a jackknifedL-statistic is described and the pseudovalue-based variance estimate is shown to be consistent under moderate smoothness and a trimming condition on the weight function. The results extend easily to functions ofL-statistics and thus answer a question posed in Miller (1974). Monte Carlo results support small-sample applicability of the large-sample results for the construction of approximate confidence intervals.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477862

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

Tests for symmetry of a distribution function about an unknown value are investigated. The asymptotic distributions under symmetry as well as under near alternatives are given for two kinds of tests: trimmed Wilcoxon tests and tests based on gaps. It turns out that these procedures have a good stability of level under unimodal densities. In a special case (skewness to the left) the efficacies have been calculated. Monte Carlo results are also given.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477863

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477864

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

Consider the familiar linear modely=Xβ +e.In an ingenious article by Sielken and Hartley (1973) it was shown how one can construct a computational scheme that will generate unbiasedL1orL∞estimators even when multiple solutions exist. In a recent paper, Harvey (1978) provides a simple proof of unbiasedness forLpestimators, 1 <p< ∞, under the assumption that the linear model is of full column rank; this insures that the estimator is unique.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477865

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

摘要:

An easily computable predictive density is considered. Although involving a plain maximum likelihood technique, it coincides with the posterior predictive density with a noninformative prior. For standard situations achievements are comparable with classical inference.

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477866

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477867

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1982.10477868

出版商:Taylor & Francis Group    

年代:1982

数据来源: Taylor