论文检索

摘要暂缺

摘要暂缺

帕累托数据的贝叶斯估计与预测AI 解读

Data from a classical Pareto distribution are to be used to make inferences about the inequality and precision parameters. In addition, it is desired to predict the behavior of further observations from the distribution. Three typical data configurations are considered (iid and two types of censoring). Dependent conjugate prior analyses are reviewed and are compared with an analysis involving independent priors for the inequality and precision parameters. It is argued that…

摘要暂缺

具有指数条件分布的二元分布AI 解读

It is frequently easier to visualize conditional distributions of experimental variables rather than joint distributions. In this article we consider the most general class of bivariate distributions such that both sets of conditional densities are exponential. The class proves to be remarkably simple to describe: The joint density must be proportional to exp(- λx - μy - νxy), where the constant of proportionality depends on the classical exponential integral. The joint…

利用强单峰分布生成有序洛伦兹曲线族AI 解读

From any strongly unimodal density on the real line, it is possible to generate a one-parameter family of Lorenz curves. The resulting families of Lorenz curves are Lorenz ordered with respect to the indexing parameter. Symmetry of the unimodal density results in the generation of symmetric Lorenz curves. A related characterization of the normal distribution is presented.

摘要暂缺

纳入复杂度成本时预检验估计量的可容许性AI 解读

Consider the problem of estimating the mean by using a random sample from a normal population. Let denote the sample mean and consider the estimator that assumes the value zero when and the value when . This is called a preliminary test estimator. For most of the usual loss functions it is inadmissible. In this article we show that for some loss functions, which include a complexity cost, the estimator is admissible. The results are related to Cohen's work on hybrid…

摘要暂缺

利用次序统计量矩集合刻画分布AI 解读

It is known that the set $M = \{\mu_{r,n} : r = 1, 2, \cdots, n; n = 1, 2, 3, \cdots\}$ of expectations of order statistics of samples from a distribution $F$ completely determines $F$ (see, for example, Hoeffding (1953)). In addition Chan (1967), Konheim (1971) and Pollak (1973) have shown that certain subsets of $M$ determine $F$. In this note conditions are derived which are sufficient for a proper subset of $M$ to determine $F$.

包含初步显著性检验的假设检验AI 解读

Suppose that independent samples are available from two normal populations with means μ1 and μ2 and known variances. It is desired to test H: μ1 =0. A possible preliminary test procedure in this case consists of first testing μ1 = μ2 and then testing H using either the sample mean from the first population or the pooled sample mean depending on the outcome of the test regarding equality of μ1 and μ2. This procedure is shown to be biased. A power comparison of this procedure…

纳入预先显著性检验的假设检验AI 解读

Suppose that independent samples are available from two normal populations with means μ1 and μ2 and known variances. It is desired to test H: μ1 =0. A possible preliminary test procedure in this case consists of first testing μ1 = μ2 and then testing H using either the sample mean from the first population or the pooled sample mean depending on the outcome of the test regarding equality of μ1 and μ2. This procedure is shown to be biased. A power comparison of this procedure…

多元指数分布的参数估计AI 解读

Simple consistent estimates are derived for the parameters of a multivariate exponential distribution. The mean squared error of the estimates is computed, and a lower bound for their efficiency is derived. A precise lower bound for the efficiency is computed in the bivariate case. It does not seem feasible to compute the efficiency precisely in the multivariate case. Some attention is given to the behavior of the estimates when observations are rounded prior to analysis.

关于具有指定边际分布的多元分布的一则注记AI 解读

A method for generating multivariate distributions with marginals in a prescribed family is introduced. It is a straightforward generalization of a method due to Marshall and Olkin.

具有指定边际分布的多元分布构造说明AI 解读

A method for generating multivariate distributions with marginals in a prescribed family is introduced. It is a straightforward generalization of a method due to Marshall and Olkin.