On the construction of m-associate PBIB designs I

The m-associate triangular association scheme has been discussed, and several series of partially balanced incomplete block (PBIB) designs with m-associate triangular association scheme have been obtained in Section 1. In Section 2, an m-associate triangular-group divisible association scheme $\text{(T}{}_{\mathrm{q}}\text{—}\text{GD}\text{m}\text{, 1<q<m)}$ has been introduced and several series of PBIB designs with m-associate triangular group divisible association scheme, from m-associate triangular PBIB designs have been constructed. Some numerical values to the three associate triangular designs, and three associate triangular group divisible designs in the range b, v≦100; r, k≦10, with their average efficiencies are given, respectively, in Tables I and II, in Section 3, where as usual v denotes the number of treatments, b the number of blocks, r the number of replications of each treatment, and k the block size.     

The Price Effects of Family Caps on Fertility Decisions of Poor Women

ABSTRACT

Family Caps have been a popular welfare reform policy designed to discourage women on welfare from bearing additional children. It has been thought that the principal mechanism through which a Cap achieves its objective of lower birth rates is the financial pressure placed on women by the denial of cash benefits. Our study uses instrumental variables Probit modeling to directly measure the contribution that price makes to Cap impact on births. We reexamine data from the New Jersey Family Development Program (n = 8,393) experiment and find that only a very small percentage (about 2.5%) of the overall Family Cap effect reported in earlier studies can be attributed to price. Moreover, the price effect holds only for short-term Black welfare recipients. We speculate that much of the unexplained Family Cap treatment effect stems from a message of social pressure and disapproval toward welfare receipt and childbearing on welfare. We offer a possible direction for future research which would directly measure the social disapproval component.     

Joint modeling tumor burden and time to event data in oncology trials

The tumor burden (TB) process is postulated to be the primary mechanism through which most anticancer treatments provide benefit. In phase II oncology trials, the biologic effects of a therapeutic agent are often analyzed using conventional endpoints for best response, such as objective response rate and progression‐free survival, both of which causes loss of information. On the other hand, graphical methods including spider plot and waterfall plot lack any statistical inference when there is more than one treatment arm. Therefore, longitudinal analysis of TB data is well recognized as a better approach for treatment evaluation. However, longitudinal TB process suffers from informative missingness because of progression or death. We propose to analyze the treatment effect on tumor growth kinetics using a joint modeling framework accounting for the informative missing mechanism. Our approach is illustrated by multisetting simulation studies and an application to a nonsmall‐cell lung cancer data set. The proposed analyses can be performed in early‐phase clinical trials to better characterize treatment effect and thereby inform decision‐making. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian analysis of life-testing problems involving the weibull distribution

Credible and highest posterior density intervals for the reliability function and parameters of a two-parameter Weibull process are obtained and the estimates compared with their corresponding classical counterparts.     

Unbiased Estimation of the Distribution Function of an Exponential Population Using Order Statistics with Application in Ranked Set Sampling

In this paper we consider the problem of unbiased estimation of the distribution function of an exponential population using order statistics based on a random sample. We present a (unique) unbiased estimator based on a single, say ith, order statistic and study some properties of the estimator for i = 2. We also indicate how this estimator can be utilized to obtain unbiased estimators when a few selected order statistics are available as well as when the sample is selected following an alternative sampling procedure known as ranked set sampling. It is further proved that for a ranked set sample of size two, the proposed estimator is uniformly better than the conventional nonparametric unbiased estimator, further, for a general sample size, a modified ranked set sampling procedure provides an unbiased estimator uniformly better than the conventional nonparametric unbiased estimator based on the usual ranked set sampling procedure.     

Gender Differences in Emotion Expression in Low-Income Adolescents Under Stress

Gender roles in mainstream US culture suggest that girls express more happiness, sadness, anxiety, and shame/embarrassment than boys, while boys express more anger and externalizing emotions, such as contempt. However, gender roles and emotion expression may be different in low-income and ethnically diverse families, as children and parents are often faced with greater environmental stressors and may have different gender expectations. This study examined gender differences in emotion expression in low-income adolescents, an understudied population. One hundred and seventy nine adolescents (aged 14–17) participated in the Trier Social Stress Test (TSST). Trained coders rated adolescents’ expressions of happiness, sadness, anxiety, shame/embarrassment, anger, and contempt during the TSST using a micro-analytic coding system. Analyses showed that, consistent with gender roles, girls expressed higher levels of happiness and shame than boys; however, contrary to traditional gender roles, girls showed higher levels of contempt than boys. Also, in contrast to cultural stereotypes, there were no differences in anger between boys and girls. Findings suggest gender-role inconsistent displays of externalizing emotions in low-income adolescents under acute stress, and may reflect different emotion socialization experiences in this group.     

Functional Mixed Effects Model for Small Area Estimation

Functional data analysis has become an important area of research because of its ability of handling high‐dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models and, in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area‐level data and fit a varying coefficient linear mixed effect model where the varying coefficients are semiparametrically modelled via B‐splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.     

Efficient Computation of Reduced Regression Models

We consider settings where it is of interest to fit and assess regression submodels that arise as various explanatory variables are excluded from a larger regression model. The larger model is referred to as the full model; the submodels are the reduced models. We show that a computationally efficient approximation to the regression estimates under any reduced model can be obtained from a simple weighted least squares (WLS) approach based on the estimated regression parameters and covariance matrix from the full model. This WLS approach can be considered an extension to unbiased estimating equations of a first-order Taylor series approach proposed by Lawless and Singhal. Using data from the 2010 Nationwide Inpatient Sample (NIS), a 20% weighted, stratified, cluster sample of approximately 8 million hospital stays from approximately 1000 hospitals, we illustrate the WLS approach when fitting interval censored regression models to estimate the effect of type of surgery (robotic versus nonrobotic surgery) on hospital length-of-stay while adjusting for three sets of covariates: patient-level characteristics, hospital characteristics, and zip-code level characteristics. Ordinarily, standard fitting of the reduced models to the NIS data takes approximately 10 hours; using the proposed WLS approach, the reduced models take seconds to fit.