A Note on Estimation of Several Missing Values in Two Way Classified Data with Many Observations Per Cell

In this paper an attempt has been made to reduce the computational complexities involved in estimation of several missing values. As a result it has been shown that one can estimate m missing values by developing only k (≤m) linear equations, where m and k are respectively the number of missing values and missing cells. The procedure is also illustrated with the help of a numerical example.     

A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets imputed under our model can be investigated in additional subsequent analyses, our method will be useful for preparing data for applications in diverse contexts in population genetics and molecular ecology.     

Comparison of Resubstitution,Data Splitting,the Bootstrap,and the Jackknife as Methods for Estimating Validity Indices of New Marker Tests: A Monte Carlo Study

Estimating the diagnostic efficiency of marker tests on the basis of the training set is an intricate problem of discriminant analysis for which no analytical solution exists. The paper outline the problem, describes various popular estimation procedures and presents the results of computer simulations comparing the estimators with respect to both bias and variance.     

一种评价脑循环动力学综合功能的新方法

目的通过对与脑循环功能相关的12个血液动力学参数进行分析,得出能够反映脑循环总体功能的一个综合指标。方法采用病例对照方法,获得120例脑血管疾病患者和130例正常者对照的流行病学资料,年龄在20岁至70岁。综合应用主成分分析、logistic回归分析等方法得到四种综合指标计算模型,并将这四种模型在临床上通过ROC曲线进行初步检验评价。结果在对原始计算数据250例人群的检验中,四个综合指标的ROC曲线下面积分别为0．905、0．901、0．942、0．911。在临床上对另外的年龄为20岁至70岁的775例人群的检验中,四个综合指标的ROC曲线下面积分别为0．943、0．940、0．969和0．945;在对年龄为50岁至60岁的144例人群的检验中,四个综合指标的ROC曲线下面积分别为0．866、0．862、0．935和0．847。讨论对12个参数进行主成分回归后选取前三个主成分进行logistic回归分析后得到的评价模型作为反应脑循环总体功能的综合指标具有较佳的评价能力与更好的稳定性。     

A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.