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通过扰动对称性生成分布：侧重多元偏斜t分布AI 解读

Summary A fairly general procedure is studied to perturb a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is sufficiently general to encompass some recent proposals in the literature, variously related to the skew normal distribution. The special case of skew elliptical densities is examined in detail, establishing connections with existing similar work. The final part of the paper…

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利用非参数回归检验线性关系AI 解读

SUMMARY The problem of checking the linearity of a regression relationship is addressed through the idea of smoothing of a residual plot. A pseudolikelihood ratio test statistic, which measures the distance between the nonparametric and the parametric models, is derived as a ratio of quadratic forms. The distribution of this statistic under the null hypothesis of linearity is calculated numerically by using Johnson curves. A power study shows the new statistic to be more…

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自回归误差下重复测量的若干最优设计AI 解读

A number of individuals are observed at equally spaced time points; different individuals behave independently but the observations on the same individual have the autocorrelation structure of a first-order autoregressive process. We are concerned with the optimal allocation of treatments, assuming that at each time point a new selection must be made. Conditions for optimality are given for models without nuisance parameters, and also with covariates and with block effects.

平稳随机过程的 m 阶最大似然估计AI 解读

SUMMARY Sometimes the likelihood cannot be computed even introducing approximations whose effect is asymptotically negligible. To overcome this problem for inference from a stochastic process, we approximate the log likelihood by a sum whose generic term is the density function of the corresponding sample element conditional on the m most recent observations, for some m ≥ 0. General results on the asymptotic properties of the associated estimates are given, and numerical…

平稳随机过程的 m 阶最大似然估计AI 解读

SUMMARY Sometimes the likelihood cannot be computed even introducing approximations whose effect is asymptotically negligible. To overcome this problem for inference from a stochastic process, we approximate the log likelihood by a sum whose generic term is the density function of the corresponding sample element conditional on the m most recent observations, for some m ≥ 0. General results on the asymptotic properties of the associated estimates are given, and numerical…