The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

PCA likelihood ratio test approach for attributed social networks monitoring

Abstract

One of the most important factors in building and changing communication mechanisms in social networks is considering features of the members of social networks. Most of the existing methods in network monitoring don’t consider effects of features in network formation mechanisms and others don’t lead to reliable results when the features abound or when there are correlations among them. In this article, we combined two methods principal component analysis (PCA) and likelihood method to monitor the underlying network model when the features of individuals abound and when some of them have high correlations with each other.     

The quantile-based flattened logistic distribution: Some properties and applications

In this paper, the quantile-based flattened logistic distribution has been studied. Some classical and quantile-based properties of the distribution have been obtained. Closed form expressions of L-moments, L-moment ratios and expectation of order statistics of the distribution have been obtained. A quantile-based analysis concerning the method of matching L-moments estimation is employed to estimate the parameters of the proposed model. We further derive the asymptotic variance–covariance matrix of the matching L-Moments estimators of the proposed model. Finally, we apply the proposed model to simulated as well as two real life datasets and compare the fit with the logistic distribution.     

Grandparents providing care for grandchildren and employment status of grandparents in South Korea

This study examines the association between providing care for grandchildren and the economic status of grandparents, focusing on the employment status. This study asks two questions. First, is providing care for grandchildren related to Korean grandparents’ employment status? Second, are the intensities of providing care for grandchildren related to grandparents’ employment status? In examining these research questions, this study focuses on gender and caregiving intensity. The findings suggest that providing care for grandchildren was associated with Korean grandmothers’ employment status. In addition, there are different relationships between providing care for grandchildren and grandparents’ employment status according to the caregiving intensities.     

农用承包地流转意愿的实证研究--基于福建省六地市农户问卷调查

从农户意愿角度，以福建省农民的农用承包地为研究对象，对影响农户承包地流转的因素进行分析，通过实地调查和问卷调查的形式收集到影响农户流转承包地的样本数据，并对通过建立二元Logistic模型对各个因素的显著性进行检验。通过实证研究发现，户主受教育程度、户主的职业、农户家庭的人口、家庭的收入主要来源、承包地面积和农户流转承包地的租金水平是农户流转承包地的显著性影响因素，其中户主受教育程度、家庭的收入主要来源、承包地面积和租金水平是正向影响，户主的职业和农户家庭的人口是负向影响。根据研究的结论提出相应的政策建议。     

“一带一路”背景下中国区域物流网络模型研究

选取“一带一路”重点涉及的五大区域18个地区为研究对象,利用改进引力模型测量区域物流引力大小及地区引力总量,构建区域物流网络模型。在明确现有模型的特点和“一带一路”对物流网络目标要求的基础上,考虑区域物流发展水平和区域间差异情况,给出优化方案及优化后的物流网络模型,指出中国区域物流网络存在的问题并给出合理化建议,旨为政府决策提供参考。     

Bayesian adaptive dose‐escalation procedures for binary and continuous responses utilizing a gain function

One of the main aims of early phase clinical trials is to identify a safe dose with an indication of therapeutic benefit to administer to subjects in further studies. Ideally therefore, dose‐limiting events (DLEs) and responses indicative of efficacy should be considered in the dose‐escalation procedure. Several methods have been suggested for incorporating both DLEs and efficacy responses in early phase dose‐escalation trials. In this paper, we describe and evaluate a Bayesian adaptive approach based on one binary response (occurrence of a DLE) and one continuous response (a measure of potential efficacy) per subject. A logistic regression and a linear log‐log relationship are used respectively to model the binary DLEs and the continuous efficacy responses. A gain function concerning both the DLEs and efficacy responses is used to determine the dose to administer to the next cohort of subjects. Stopping rules are proposed to enable efficient decision making. Simulation results shows that our approach performs better than taking account of DLE responses alone. To assess the robustness of the approach, scenarios where the efficacy responses of subjects are generated from an E _max model, but modelled by the linear log–log model are also considered. This evaluation shows that the simpler log–log model leads to robust recommendations even under this model showing that it is a useful approximation to the difficulty in estimating E _max model. Additionally, we find comparable performance to alternative approaches using efficacy and safety for dose‐finding. Copyright © 2015 John Wiley & Sons, Ltd.     

Threshold‐free estimation of functional antibody titers of a group B streptococcus opsonophagocytic killing assay

Opsonophagocytic killing assays (OPKA) are routinely used for the quantification of bactericidal antibodies in blood serum samples. Quantification of the OPKA readout, the titer, provides the basis for the statistical analysis of vaccine clinical trials having functional immune response endpoints. Traditional OPKA titers are defined as the maximum serum dilution yielding a predefined bacterial killing threshold value, and they are estimated by fitting a dose‐response model to the dilution‐killing curve. This paper illustrates a novel definition of titer, the threshold‐free titer, which preserves biological interpretability while not depending on any killing threshold or on a postulated shape of the dose‐response curve. These titers are shown to be more precise than the traditional threshold‐based titers when using simulated and experimental group B streptococcus OPKA experimental data. Also, titer linearity is shown to be not measurable when using threshold‐based titers, whereas it becomes measurable using threshold‐free titers. The biological interpretability and operational characteristics demonstrated here indicate that threshold‐free titers are an appropriate tool for the routine analysis of OPKA data. Copyright © 2015 John Wiley & Sons, Ltd.