日期精度：到日 2 篇 · 到月 4 篇 · 到年 0 篇
一个不一致的最大似然估计
An example is given of a family of distributions on [— 1, 1] with a continuous one-dimensional parameterization that joins the triangular distribution (when Θ = 0) to the uniform (when Θ = 1), for which the maximum likelihood estimates exist and converge strongly to Θ = 1 as the sample size tends to infinity, whatever be the true value of the parameter. A modification that satisfies Cramér's conditions is also given.
一个不一致的最大似然估计
An example is given of a family of distributions on [— 1, 1] with a continuous one-dimensional parameterization that joins the triangular distribution (when Θ = 0) to the uniform (when Θ = 1), for which the maximum likelihood estimates exist and converge strongly to Θ = 1 as the sample size tends to infinity, whatever be the true value of the parameter. A modification that satisfies Cramér's conditions is also given.
基于删失数据的贝叶斯非参数估计
Let $X_1, \cdots, X_n$ be a random sample from an unknown $\operatorname{cdf} F$, let $y_1, \cdots, y_n$ be known real constants, and let $Z_i = \min(X_i, y_i), i = 1, \cdots, n$. It is required to estimate $F$ on the basis of the observations $Z_1, \cdots, Z_n$, when the loss is squared error. We find a Bayes estimate of $F$ when the prior distribution of $F$ is a process neutral to the right. This generalizes results of Susarla and Van Ryzin who use a Dirichlet process…
样本量为三和四时柯西分布参数的最大似然估计
Expressions are given for the joint maximum likelihood estimates of the location and scale parameters of a Cauchy distribution based on samples of size 3 and 4.
样本量为 3 和 4 时柯西分布参数的最大似然估计
Expressions are given for the joint maximum likelihood estimates of the location and scale parameters of a Cauchy distribution based on samples of size 3 and 4.
若干非参数问题的贝叶斯分析
The Bayesian approach to statistical problems, though fruitful in many ways, has been rather unsuccessful in treating nonparametric problems. This is due primarily to the difficulty in finding workable prior distributions on the parameter space, which in nonparametric ploblems is taken to be a set of probability distributions on a given sample space. There are two desirable properties of a prior distribution for nonparametric problems. (I) The support of the prior…