Computations for the familial analysis of binary traits

For familial aggregation of a binary trait, one method that has been used is the GEE2 (generalized estimating equation) method corresponding to a multivariate logit model. We solve the complex estimating equations for the GEE2 method using an automatic differentiation software which computes the derivatives of a function numerically using the chain rule of the calculus repeatedly on the elementary operations of the function. Based on this, we are able to show in a simulation study that the GEE2 estimates are quite close to the maximum likelihood estimates assuming a multivariate logit model, and that the GEE2 method is computationally faster when the dimension or family size is larger than four.     

Weighted estimating equation: modified GEE in longitudinal data analysis

The method of generalized estimating equations （GEE） introduced by K. Y. Liang and S. L. Zeger has been widely used to analyze longitudinal data. Recently, this method has been criticized for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. In this paper, we present a new method named as ＇weighted estimating equations （WEE）＇ for estimating the correlation parameters. The new estimates of correlation parameters are obtained as the solutions of these weighted estimating equations. For some commonly assumed correlation structures, we show that there exists a unique feasible solution to these weighted estimating equations regardless the correlation structure is correctly specified or not. The new feasible estimates of correlation parameters are consistent when the working correlation structure is correctly specified. Simulation results suggest that the new method works well in finite samples.     

Exact pseudopolynomial algorithms for a balanced 2-clustering problem

We consider the strongly NP-hard problem of partitioning a set of Euclidean points into two clusters so as to minimize the sum (over both clusters) of the weighted sum of the squared intracluster distances from the elements of the clusters to their centers. The weights of sums are the sizes of the clusters. The center of one cluster is given as input, while the center of the other cluster is unknown and determined as the average value over all points in the cluster (as the geometric center). Two variants of the problems are analyzed in which the cluster sizes are either given or unknown. We present and prove some exact pseudopolynomial algorithms in the case of integer components of the input points and fixed space dimension.     

Semiparametric estimation in regression with missing covariates using single-index models

We investigate semiparametric estimation of regression coefficients through generalized estimating equations with single-index models when some covariates are missing at random. Existing popular semiparametric estimators may run into difficulties when some selection probabilities are small or the dimension of the covariates is not low. We propose a new simple parameter estimator using a kernel-assisted estimator for the augmentation by a single-index model without using the inverse of selection probabilities. We show that under certain conditions the proposed estimator is as efficient as the existing methods based on standard kernel smoothing, which are often practically infeasible in the case of multiple covariates. A simulation study and a real data example are presented to illustrate the proposed method. The numerical results show that the proposed estimator avoids some numerical issues caused by estimated small selection probabilities that are needed in other estimators.

     

Additive hazards regression with random effects for clustered failure times

Additive hazards model with random effects is proposed for modelling the correlated failure time data when focus is on comparing the failure times within clusters and on estimating the correlation between failure times from the same cluster, as well as the marginal regression parameters. Our model features that, when marginalized over the random effect variable, it still enjoys the structure of the additive hazards model. We develop the estimating equations for inferring the regression parameters. The proposed estimators are shown to be consistent and asymptotically normal under appropriate regularity conditions. Furthermore, the estimator of the baseline hazards function is proposed and its asymptotic properties are also established. We propose a class of diagnostic methods to assess the overall fitting adequacy of the additive hazards model with random effects. We conduct simulation studies to evaluate the finite sample behaviors of the proposed estimators in various scenarios. Analysis of the Diabetic Retinopathy Study is provided as an illustration for the proposed method.     

An optimal modification of the LIML estimation for many instruments and persistent heteroscedasticity

We consider the estimation of coefficients of a structural equation with many instrumental variables in a simultaneous equation system. It is mathematically equivalent to the estimating equations estimation or a reduced rank regression in the statistical multivariate linear models when the number of restrictions or the dimension of estimating equations increases with the sample size. As a semi-parametric method, we propose a class of modifications of the limited information maximum likelihood (LIML) estimator to improve its asymptotic properties as well as the small sample properties for many instruments and persistent heteroscedasticity. We show that an asymptotically optimal modification of the LIML estimator, which is called AOM-LIML, improves the LIML estimator and other estimation methods. We give a set of sufficient conditions for an asymptotic optimality when the number of instruments or the dimension of the estimating equations is large with persistent heteroscedasticity including a case of many weak instruments.     

Two-step Estimation for Longitudinal Data When the Working Correlation Matrix is a Linear Combination of Some Known Matrices

Acta Mathematicae Applicatae Sinica, English Series - The generalized estimating equations(GEE) approach is perhaps one of the most widely used methods for longitudinal data analysis. While the GEE...     

Comparison of methods for ordinal lens opacity data from atomic-bomb survivors: univariate worse-eye method and bivariate GEE method using global odds ratio

In analyses of bivariate ordered polytomous cataract data from atomic-bomb survivors, we compared two methods, the univariate worse-eye method, and the bivariate generalized estimating equations (GEE’s) method using global odds ratio by Williamson et al. (Journal of the American Statistical Association, 90, 1432–1437, 1995). When the association was large and only subject level covariates were used, model selection in the univariate and bivariate methods resulted in the same mean model and similar risk estimates. We showed that the mean parameter and the standard error (SE) in the univariate model are emphasized relative to those in the bivariate model, the biases of which are negligible when the association between both eyes is large. Large sample simulation studies indicated that the univariate Wald statistics are slightly conservative. The simulations also showed that, in bivariate cases, irrespective of the degree of association, the independence estimating equations method with robust SE, and the GEE method with model-based and robust SE are almost fully efficient in parameter estimation when only subject level covariates are included in the mean.     

A Novel Approach for Markov Random Field With Intractable Normalizing Constant on Large Lattices

The pseudo likelihood method of Besag (1974) has remained a popular method for estimating Markov random field on a very large lattice, despite various documented deficiencies. This is partly because it remains the only computationally tractable method for large lattices. We introduce a novel method to estimate Markov random fields defined on a regular lattice. The method takes advantage of conditional independence structures and recursively decomposes a large lattice into smaller sublattices. An approximation is made at each decomposition. Doing so completely avoids the need to compute the troublesome normalizing constant. The computational complexity is O(N), where N is the number of pixels in the lattice, making it computationally attractive for very large lattices. We show through simulations, that the proposed method performs well, even when compared with methods using exact likelihoods. Supplementary material for this article is available online.     

纵向数据非参数混合效应模型的一个局部不变估计

非参数核回归方法近年来已被用于纵向数据的分析(Lin和Carroll,2000)．一个颇具争议性的问题是在非参数核回归中是否需要考虑纵向数据间的相关性．Lin和Carroll (2000)证明了基于独立性(即忽略相关性)的核估计在一类核GEE估计量中是(渐近)最有效的．基于混合效应模型方法作者提出了一个不同的核估计类,它自然而有效地结合了纵向数据的相关结构．估计量达到了与Lin和Carroll的估计量相同的渐近有效性,且在有限样本情形下表现更好．由此方法可以很容易地获得对于总体和个体的非参数曲线估计．所提出的估计量具有较好的统计性质,且实施方便,从而对实际工作者具有较大的吸引力．     

带有弹性碰撞的离散的凝结方程

带有弹性碰撞的离散的凝结方程是反映粒子增长动力学的数学模型，它刻划了这样一种粒子反应系统；系统中任意两个粒子碰撞后一定的概率或者凝结成为更大的粒子，或者发生弹性碰撞．本文研究了这一系统发生冻肢的可能性，并给出了一个充分条件．     

On complexity of some problems of cluster analysis of vector sequences

NP-completeness of two clustering (partition) problems is proved for a finite sequence of Euclidean vectors. In the optimization versions of both problems it is required to partition the elements of the sequence into a fixed number of clusters minimizing the sum of squares of the distances from the cluster elements to their centers. In the first problem the sizes of clusters are the part of input, while in the second they are unknown (they are the variables for optimization). Except for the center of one (special) cluster, the center of each cluster is the mean value of all vectors contained in it. The center of the special cluster is zero. Also, the partition must satisfy the following condition: The difference between the indices of two consecutive vectors in every nonspecial cluster is bounded below and above by two given constants.     

Efficient computation of generalized median estimators

The generalized median (GM) estimator is a family of robust estimators that balances the competing demands of statistical efficiency and robustness. By choosing a kernel that is efficient for the parameter, the GM estimator gains robustness by computing the median of the kernel evaluated at all possible subsets from the sample. The GM estimator is often computationally infeasible because the number of subsets can be large for even modest sample sizes. Writing the estimator in terms of the quantile function facilitates an approximation using a sample of all possible subsets. While both sampling with and without replacement are feasible, sampling without replacement is preferred because of the reduction in variance from the sampling fraction. The proposed algorithm uses sequential sampling to compute an approximation within a user-chosen margin of error.     

Variance estimation using judgment post-stratification

We consider the problem of estimating the variance of a population using judgment post-stratification. By conditioning on the observed vector of ordered in-stratum sample sizes, we develop a conditionally unbiased nonparametric estimator that outperforms the sample variance except when the rankings are very poor. This estimator also outperforms the standard unbiased nonparametric variance estimator from unbalanced ranked-set sampling.     

A fully polynomial-time approximation scheme for a sequence 2-cluster partitioning problem

We consider a strongly NP-hard problem of partitioning a finite sequence of points in Euclidean space into the two clustersminimizing the sum over both clusters of intra-cluster sums of squared distances from the clusters elements to their centers. The sizes of the clusters are fixed. The centroid of the first cluster is defined as the mean value of all vectors in the cluster, and the center of the second cluster is given in advance and equals 0. Additionally, the partition must satisfy the restriction that for all vectors in the first cluster the difference between the indices of two consequent points from this cluster is bounded from below and above by some given constants.We present a fully polynomial-time approximation scheme for the case of fixed space dimension.     

Shrinkage estimation analysis of correlated binary data with a diverging number of parameters

For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.     

A clustering approach to the planar hub location problem

The problem tackled in this paper is as follows: consider a set ofn interacting points in a two-dimensional space. The levels of interactions between the observations are given exogenously. It is required to cluster then observations intop groups, so that the sum of squared deviations from the cluster means is as small as possible. Further, assume that the cluster means are adjusted to reflect the interaction between the entities. (It is this latter consideration which makes the problem interesting.) A useful property of the problem is that the use of a squared distance term yields a linear system of equations for the coordinates of the cluster centroids. These equations are derived and solved repeatedly for a given set of cluster allocations. A sequential reallocation of the observations between the clusters is then performed. One possible application of this problem is to the planar hub location problem, where the interacting observations are a system of cities and the interaction effects represent the levels of flow or movement between the entities. The planar hub location problem has been limited so far to problems with fewer than 100 nodes. The use of the squared distance formulation, and a powerful supercomputer (Cray Y-MP) has enabled quick solution of large systems with 250 points and four groups. The paper includes both small illustrative examples and computational results using systems with up to 500 observations and 9 clusters.     

离散的非线性爆炸方程的密度守恒解

离散的非线性爆炸方程是刻划粒子增长动力学的数学模型，这一模型反映了一类粒子反应系统中各种粒子密度随时间变化的规律，它是由可数无限多个彼此相互关联的非线性常微分方程所组成的自治系统。本文研究了这一无限维系统的密度守恒解的存在性。     

On the complexity of some Euclidean problems of partitioning a finite set of points

Problems of partitioning a finite set of Euclidean points (vectors) into clusters are considered. The criterion is to minimize the sum, over all clusters, of (1) squared norms of the sums of cluster elements normalized by the cardinality, (2) squared norms of the sums of cluster elements, and (3) norms of the sum of cluster elements. It is proved that all these problems are strongly NP-hard if the number of clusters is a part of the input and are NP-hard in the ordinary sense if the number of clusters is not a part of the input (is fixed). Moreover, the problems are NP-hard even in the case of dimension 1 (on a line).     

NP-Hardness of Some Euclidean Problems of Partitioning a Finite Set of Points

Problems of partitioning a finite set of Euclidean points (vectors) into clusters are considered. The criterion is to minimize the sum, over all clusters, of (1) squared norms of the sums of cluster elements normalized by the cardinality, (2) squared norms of the sums of cluster elements, and (3) norms of the sum of cluster elements. It is proved that all these problems are strongly NP-hard if the number of clusters is a part of the input and are NP-hard in the ordinary sense if the number of clusters is not a part of the input (is fixed). Moreover, the problems are NP-hard even in the case of dimension 1 (on a line).