Bayesian analysis of multivariate mixed longitudinal ordinal and continuous data [0.03%]
多元混合纵向有序和连续数据的贝叶斯分析
Xiao Zhang
Xiao Zhang
Multivariate longitudinal ordinal and continuous data exist in many scientific fields. However, it is a rigorous task to jointly analyse them due to the complicated correlated structures of those mixed data and the lack of a multivariate di...
Makoto Aoshima,Dan Shen,Haipeng Shen et al.
Makoto Aoshima et al.
Peter Hall's work illuminated many aspects of statistical thought, some of which are very well known including the bootstrap and smoothing. However, he also explored many other lesser known aspects of mathematical statistics. This is a surv...
Iain M Johnstone
Iain M Johnstone
Consider the classical Gaussian unitary ensemble of size N and the real white Wishart ensemble with N variables and n degrees of freedom. In the limits of large N and n, with positive ratio γ in the Wishart case, the expected number of eig...
An Improved Test of Equality of Mean Directions for the Langevin-von Mises-Fisher Distribution [0.03%]
改进的Langevin-von Mises-Fisher分布均值方向相等性检验方法
Pavlina Rumcheva,Brett Presnell
Pavlina Rumcheva
A multi-sample test for equality of mean directions is developed for populations having Langevin-von Mises-Fisher distributions with a common unknown concentration. The proposed test statistic is a monotone transformation of the likelihood ...
Paul H Garthwaite,Inge Koch
Paul H Garthwaite
Quadratic forms capture multivariate information in a single number, making them useful, for example, in hypothesis testing. When a quadratic form is large and hence interesting, it might be informative to partition the quadratic form into ...
COVARIATE DECOMPOSITION METHODS FOR LONGITUDINAL MISSING-AT-RANDOM DATA AND PREDICTORS ASSOCIATED WITH SUBJECT-SPECIFIC EFFECTS [0.03%]
处理与受试者特定效果相关预测变量的纵向随机缺失数据的协变量分解方法
John M Neuhaus,Charles E McCulloch
John M Neuhaus
Investigators often gather longitudinal data to assess changes in responses over time within subjects and to relate these changes to within-subject changes in predictors. Missing data are common in such studies and predictors can be correla...
Pulak Ghosh,Timothy Hanson
Pulak Ghosh
We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees pr...
NONPARAMETRIC ESTIMATION OF CONDITIONAL CUMULATIVE HAZARDS FOR MISSING POPULATION MARKS [0.03%]
缺失人口标记的条件累积危险率的非参数估计方法研究
Dipankar Bandyopadhyay,Amalia Jácome Pumar
Dipankar Bandyopadhyay
A new function for the competing risks model, the conditional cumulative hazard function, is introduced, from which the conditional distribution of failure times of individuals failing due to cause j can be studied. The standard Nelson-Aale...
Empirical Likelihood Based Inferences for Partially Linear Models with Missing Covariates [0.03%]
基于经验似然的缺失协变量部分线性模型的统计推断方法研究
Hua Liang,Yongsong Qin
Hua Liang
This paper considers statistical inference for partially linear models Y = X(T)mu + nu(Z) + epsilon when the linear covariate X is missing with missing probability pi depending upon (Y, Z). We propose empirical likelihood based statistics t...