A Partial Empirical Likelihood Based Score Test Under a Semiparametric Finite Mixture Model

We propose a score statistic to test the null hypothesis that the two-component density functions are equal under a semiparametric finite mixture model. The proposed score test is based on a partial empirical likelihood function under an I-sample semiparametric model. The proposed score statistic has an asymptotic chi-squared distribution under the null hypothesis and an asymptotic noncentral chi-squared distribution under local alternatives to the null hypothesis. Moreover, we show that the proposed score test is asymptotically equivalent to a partial empirical likelihood ratio test and a Wald test. We present some results on a simulation study.     

缺失数据下半参数回归模型的经验似然推断

针对响应变量缺失下的半参数回归模型,构造模型中未知参数的经验对数似然比统计量,证明了所提出的统计量具有渐近χ2分布,由此构造未知参数的置信域,并就置信域的覆盖概率及区间长度方面,通过模拟研究与最小二乘法进行优劣比较.     

Penalised spline support vector classifiers: computational issues

We study computational issues for support vector classification with penalised spline kernels. We show that, compared with traditional kernels, computational times can be drastically reduced in large problems making such problems feasible for sample sizes as large as ~10⁶. The optimisation technology known as interior point methods plays a central role. Penalised spline kernels are also shown to allow simple incorporation of low-dimensional structure such as additivity. This can aid both interpretability and performance.     

连续型半参数广义线性纵向数据模型的方差成分检验

回归模型的方差成分检验是一个非常重要的问题.该文针对离差参数的变异, 随机效应的影响及两者同时具有的三种情形, 研究了基于纵向数据的连续型半参数广义线性模型的方差成分检验, 得到了Score检验统计量, 最后通过计算机模拟验证了该文所提出的方法的有效性.     

A note on the conditional density estimate in the single functional index model

In this paper, we consider the estimation of the conditional density of a scalar response variable Y, given a Hilbertian random variable X when the observations are linked with a single-index structure. We establish the pointwise and the uniform almost complete convergence (with the rate) of the kernel estimate of this model. As an application, we show how our result can be applied in the prediction problem via the conditional mode estimate. Finally, the estimation of the functional index via the pseudo-maximum likelihood method is also discussed but not tackled.     

一个有效估计:半参数非时齐扩散模型的局部线性复合分位回归估计

主要研究半参数非时齐扩散模型的参数估计问题.基于非时齐扩散模型的离散观测样本,首先得到漂移参数的局部线性复合分位回归估计,并证明估计量的渐近偏差、渐近方差和渐近正态性.其次,讨论了带宽的选择和局部线性复合分位回归估计关于局部线性最小二乘估计的渐近相对效,所得到的局部估计较局部线性最小二乘估计更为有效.最后,通过模拟说明了局部线性复合分位回归估计比局部线性最小二乘估计的模拟效果更好.     

Inference for the treatment effects in two sample problems with right-censored and length-biased data

In the study of comparing treatment effects, the data structures of two samples may be different. In this paper, we develop a unified semiparametric estimating equation approach to estimate various types of treatment effects with right-censored and length-biased data based on a semiparametric two-sample model. The large sample properties of the proposed estimators are derived and numerical studies are conducted to illustrate the proposed methods.     

A note on the identifiability of nonparametric and semiparametric mixtures of GLMs

In this article, we first propose a semiparametric mixture of generalized linear models (GLMs) and a nonparametric mixture of GLMs, and then establish identifiability results under mild conditions.     

半参数广义线性混合效应模型的估计及其渐近性质

半参数广义线性混合效应模型在心理学、生物育种、医学等领域有广泛的应用. Zhang(1998)用最大惩罚似然函数的方法(MPLE)对模型的参数和非参数部分进行了估计, 而Zhang (1998) MPLE方法只适用于正态数据模型. 对于泊松等常用的模型, 常的方法是将随机效应看作缺失数据, 再引入EM算法. 本文基于McCulloch 1997)提出的MCNR算法, 此算法推广到半参数广义线性混合效应模型中并得到相应的估计算法. 于非参数部分, 本文采用P样条拟合并利用GCV方法选取光滑参数, 时证明了所得估计的相合性和渐近正态性. 最后, 过模拟和实例与其它算法作比较验证本文估计方法的有效性.