Regression-based estimation of the false negative fraction when multiple negatives are unverified

Summary.  The paper describes a method of estimating the false negative fraction of a multiple-screening test when individuals who test negatively on all K tests do not have their true disease status verified. The method proposed makes no explicit assumptions about the underlying heterogeneity of the population or about serial correlation of test results within an individual. Rather, it is based on estimating false negative fractions conditionally on observed diagnostic histories and extrapolating the observed patterns in these empirical frequencies by using logistic regression. The method is illustrated on, and motivated by, data on a multiple-screening test for bowel cancer.     

Optimal superimposed codes and designs for Renyi''s search model

Renyi (Bull. Amer. Math. Soc. 71 (6) (1965) 809) suggested a combinatorial group testing model, in which the size of a testing group was restricted. In this model, Renyi considered the search of one defective element (significant factor) from the finite set of elements (factors). The corresponding optimal search designs were obtained by Katona (J. Combin. Theory 1 (2) (1966) 174). In the present work, we study Renyi's search model of several significant factors. This problem is closely related to the concept of binary superimposed codes, which were introduced by Kautz and Singleton (IEEE Trans. Inform Theory 10 (4) (1964) 363) and were investigated by D'yachkov and Rykov (Problems Control Inform. Theory 12 (4) (1983) 229), Erdos et al. (Israel J. Math. 51 (1–2) (1985) 75), Ruszinko (J. Combin. Theory Ser. A 66 (1994) 302) and Furedi (J. Combin. Theory Ser. A 73 (1996) 172). Our goal is to prove a lower bound on the search length and to construct the optimal superimposed codes and search designs. The preliminary results have been published by D'yachkov and Rykov (Conference on Computer Science & Engineering Technology, Yerevan, Armenia, September 1997, p. 242).     

双重二元分割:单位制变革中的城市劳动力市场

劳动力市场分割是世界各国的普遍现象,是对于劳动力市场非完全竞争的另一种表述。我国城市劳动力市场是在“单位制”变革的背景下形成的,在宏观上表现为体制内劳动力市场与体制外劳动力市场的二元分割。其中体制内劳动力市场与体制外劳动力市场又分别存在着二元分割,因此呈现出双重二元分割的状态。劳动力市场的双重二元分割在现实当中表现为我国劳动力市场中独特的就业模式和收入分配模式。这种劳动力市场的分割状态会随着“单位制”持续变革发生动态的调整和演变,其目标取向是规范统一的劳动力市场。     

失地农民生活满意度及影响因素

由于工业化和城市化进程不断加快,大量农业用地被征用为城市建设用地,导致失地农民数量不断增加。失地农民在生活、工作等方面面临许多问题,其生活满意度及其影响因素不容忽视。以四川省荣县220位失地农民的调研数据为基础,运用Binary Logistic回归分析方法分析了失地农民生活满意度的影响因素。研究结果表明：征地意愿、就业现状、是否得到就业扶持、是否有医疗保险等6个因素对失地农民生活是否满意影响为正,而征地补偿标准、住房面积、交通条件等5个因素的影响为负。在此基础上,本文从完善就业政策、健全社会保障体系、改善居住条件等方面提出相关对策建议。     

The bandpass problem: combinatorial optimization and library of problems

A combinatorial optimization problem, called the Bandpass Problem, is introduced. Given a rectangular matrix A of binary elements {0,1} and a positive integer B called the Bandpass Number, a set of B consecutive non-zero elements in any column is called a Bandpass. No two bandpasses in the same column can have common rows. The Bandpass problem consists of finding an optimal permutation of rows of the matrix, which produces the maximum total number of bandpasses having the same given bandpass number in all columns. This combinatorial problem arises in considering the optimal packing of information flows on different wavelengths into groups to obtain the highest available cost reduction in design and operating the optical communication networks using wavelength division multiplexing technology. Integer programming models of two versions of the bandpass problems are developed. For a matrix A with three or more columns the Bandpass problem is proved to be NP-hard. For matrices with two or one column a polynomial algorithm solving the problem to optimality is presented. For the general case fast performing heuristic polynomial algorithms are presented, which provide near optimal solutions, acceptable for applications. High quality of the generated heuristic solutions has been confirmed in the extensive computational experiments. As an NP-hard combinatorial optimization problem with important applications the Bandpass problem offers a challenge for researchers to develop efficient computational solution methods. To encourage the further research a Library of Bandpass Problems has been developed. The Library is open to public and consists of 90 problems of different sizes (numbers of rows, columns and density of non-zero elements of matrix A and bandpass number B), half of them with known optimal solutions and the second half, without.     

Structural changes in autoregressive models for binary time series

We study autoregressive models for binary time series with possible changes in their parameters. A procedure for detection and testing of a single change is suggested. The limiting behavior of the test statistic is derived. The performance of the test is analyzed under the null hypothesis as well as under different alternatives via a simulation study. Application of the method to a real data set on US recession is provided as an illustration.     

Imputation in Data Fusion of Heterogeneous Data Sets A Model-Based Numerical Experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.     

Bayesian Inference of Odds Ratios in Misclassified Binary Data with a Validation Substudy

We propose a fully Bayesian model with a non-informative prior for analyzing misclassified binary data with a validation substudy. In addition, we derive a closed-form algorithm for drawing all parameters from the posterior distribution and making statistical inference on odds ratios. Our algorithm draws each parameter from a beta distribution, avoids the specification of initial values, and does not have convergence issues. We apply the algorithm to a data set and compare the results with those obtained by other methods. Finally, the performance of our algorithm is assessed using simulation studies.     

Trajectory Modeling of Longitudinal Binary Data: Application of the EM Algorithm for Mixture Models

A developmental trajectory describes the course of behavior over time. Identifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. We introduce a framework for identifying trajectories by using the Expectation-Maximization (EM) algorithm to fit semiparametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algorithms (PROC TRAJ in SAS), standard EM, and two other EM-based algorithms for speeding up convergence. Simulation shows that EM methods produce more accurate parameter estimates. The EM methodology is illustrated with a longitudinal dataset involving adolescents smoking behaviors.     

Bayesian estimation of sensitivity level and population proportion of a sensitive characteristic in a binary optional unrelated question RRT model

Sihm et al. (2016 Sihm, J. S., A. Chhabra, and S. N. Gupta. 2016. An optional unrelated question RRT model. Involve: A Journal of Mathematics 9 (2):195–209.[Crossref] , [Google Scholar]) proposed an unrelated question binary optional randomized response technique (RRT) model for estimating the proportion of population that possess a sensitive characteristic and the sensitivity level of the question. In our work, decision theoretic approach has been followed to obtain Bayes estimates of the two parameters along with their corresponding minimal Bayes posterior expected losses (BPEL) using beta prior and squared error loss function (SELF). Relative losses are also examined to compare the performances of the Bayes estimates with those of the classical estimates obtained by Sihm et al. (2016 Sihm, J. S., A. Chhabra, and S. N. Gupta. 2016. An optional unrelated question RRT model. Involve: A Journal of Mathematics 9 (2):195–209.[Crossref] , [Google Scholar]). The results obtained are illustrated with the help of real survey data using non informative prior.