On MCMC sampling in hierarchical longitudinal models

Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistical models. A remedy for this problem is to block the parameters into groups, which are then updated simultaneously using either a Gibbs or Metropolis-Hastings step. In this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in non-Gaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three real-data examples.     

采用双向搜索算法的简单D0L系统反演研究

研究如何把D0L系统经过快速搜索,寻找出能够通过L系统迭代生长复原的D0L基因组.采用的方法是通过双向搜索算法寻找到基因的产生式,然后用除法运算得到完整的基因组.双向搜索算法的基本思想是在L系统字符串的头部寻找符合约束条件的字符子串,然后从尾部开始取等长字符子串进行比对,匹配后用约定条件来判定该子串是否为产生式.     

On the small sample properties of norm-restricted maximum likelihood estimators for logistic regression models

This paper develops alternatives to maximum likelihood estimators (MLE) for logistic regression models and compares the mean squared error (MSE) of the estimators. The MLE for the vector of underlying success probabilities has low MSE only when the true probabilities are extreme (i.e., near 0 or 1). Extreme probabilities correspond to logistic regression parameter vectors which are large in norm. A competing “restricted” MLE and an empirical version of it are suggested as estimators with better performance than the MLE for central probabilities. An approximate EM-algorithm for estimating the restriction is described. As in the case of normal theory ridge estimators, the proposed estimators are shown to be formally derivable by Bayes and empirical Bayes arguments. The small sample operating characteristics of the proposed estimators are compared to the MLE via a simulation study; both the estimation of individual probabilities and of logistic parameters are considered.     

A continuous latent spatial model for crack initiation in bone cement

Summary.  Hip replacements rovide a means of achieving a higher quality of life for individuals who have, through aging or injury, accumulated damage to their natural joints. This is a very common operation, with over a million people a year benefiting from the procedure. The replacements themselves fail mainly as a result of the mechanical loosening of the components of the artificial joint due to damage accumulation. This damage accumulation consists of the initiation and growth of cracks in the bone cement which is used to fixate the replacement in the human body. The data come from laboratory experiments that are designed to assess the effectiveness of the bone cement in resisting damage. We examine the properties of the bone cement, with the aim being to estimate the effect that both observable and unobservable spatially varying factors have on causing crack initiation. To do this, an explicit model for the damage process is constructed taking into account the tension and compression at different locations in the specimens. A gamma random field is used to model any latent spatial factors that may be influential in crack initiation. Bayesian inference is carried out for the parameters of this field and related covariates by using Markov chain Monte Carlo techniques.     

平台用工关系中经济从属性的内涵厘定及司法认定

数字经济革新劳动用工形式，传统经济从属性标准的解释功能弱化，已不足以支撑司法机关定性平台劳动关系，导致平台与劳动者之间劳动关系司法确认陷入困窘，平台用工劳动者权益保障面临司法救济之殇。虽然突破了传统从属性认定标准，然而平台生产过程中劳动力与生产资料相结合的劳动本质属性并未改变。由此宜立足马克思主义政治经济学视角，探究基本的劳动从属资本理论，通过数据信息、算法技术构建经济从属性新的时代内涵，使得经济从属性成为司法机关判断劳动关系存在与否的重要标尺，为保障平台弱势群体劳动权益，实现社会实质平等提供有益建议。     

设施布置问题的非线性目标规划模型及其遗传算法

由于设施布置问题实际上是一个极其复杂的多约束多目标的优化问题 ,本文将其建模为一新的非线性目标规划模型。为更快更好地求解这一模型 ,本文采用了一种新的遗传算法。它不仅能有效处理不等面积及固定设施问题 ,而且有希望解决多层布置问题。     

改进的基于听觉掩蔽特性的语音增强

该文利用人耳感知的掩蔽特性,研究了一种改进的基于人耳听觉掩蔽特性的语音增强方法。该方法改进了对含噪语音中噪声的估计,即在估计噪声时,先在帧上判断语音的有无,然后在频率点上判断语音的有无,最后再从感知的角度,利用估计出的语音信号计算掩蔽阈值,以此在时域和频域上合理地调整增强系数。仿真结果表明,该方法不仅使信噪比有较大的提高,而且减少了语音失真,同时也很好地抑制了背景噪声和残余音乐噪声。     

Model‐based linear clustering

The authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set. For each linear cluster, an errors‐in‐variables model is assumed. The optimization of the derived profile likelihood can be achieved by an EM algorithm. Its asymptotic properties and its relationships with several existing clustering methods are discussed. Methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purposes. The Canadian Journal of Statistics 38: 716–737; 2010 © 2010 Statistical Society of Canada     

A model with mixed binary responses and censored observations

In this paper, we study the maximum likelihood estimation of a model with mixed binary responses and censored observations. The model is very general and includes the Tobit model and the binary choice model as special cases. We show that, by using additional binary choice observations, our method is more efficient than the traditional Tobit model. Two iterative procedures are proposed to compute the maximum likelihood estimator (MLE) for the model based on the EM algorithm (Dempster et al, 1977) and the Newton-Raphson method. The uniqueness of the MLE is proved. The simulation results show that the inconsistency and inefficiency can be significant when the Tobit method is applied to the present mixed model. The experiment results also suggest that the EM algorithm is much faster than the Newton-Raphson method for the present mixed model. The method also allows one to combine two data sets, the smaller data set with more detailed observations and the larger data set with less detailed binary choice observations in order to improve the efficiency of estimation. This may entail substantial savings when one conducts surveys.     

Learning rates of multi-kernel regression by orthogonal greedy algorithm

We investigate the problem of regression from multiple reproducing kernel Hilbert spaces by means of orthogonal greedy algorithm. The greedy algorithm is appealing as it uses a small portion of candidate kernels to represent the approximation of regression function, and can greatly reduce the computational burden of traditional multi-kernel learning. Satisfied learning rates are obtained based on the Rademacher chaos complexity and data dependent hypothesis spaces.