Results for two-level fractional factorial designs of resolution IV or more

The main theorem of this paper shows that foldover designs are the only (regular or nonregular) two-level factorial designs of resolution IV (strength 3) or more for n   runs and n/3?m?n/2$n/3?m?n/2$ factors. This theorem is a generalization of a coding theory result of Davydov and Tombak [1990. Quasiperfect linear binary codes with distance 4 and complete caps in projective geometry. Problems Inform. Transmission 25, 265–275] which, under translation, effectively states that foldover (or even) designs are the only regular two-level factorial designs of resolution IV or more for n   runs and 5n/16?m?n/2$5n/16?m?n/2$ factors. This paper also contains other theorems including an alternative proof of Davydov and Tombak's result.     

设计包含最多纯净两因子交互效应分量条件

对于任意的素数或者素数幂 ,本文通过分析 设计中不纯净两因子交互效应(Two-Factor Interaction, 2FI)分量的个数,推导出某些 设计包含最多纯净2FI分量的一个条件。该结论对构造包含最多纯净2FI分量的 设计具有重要意义。     

Semi‐Nonparametric IV Estimation of Shape‐Invariant Engel Curves

This paper studies a shape‐invariant Engel curve system with endogenous total expenditure, in which the shape‐invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded completeness and the estimation procedure applies the sieve minimum distance estimation of conditional moment restrictions, allowing for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Root‐n asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of “low‐level” sufficient conditions. Our empirical application using the U.K. Family Expenditure Survey shows the importance of adjusting for endogeneity in terms of both the nonparametric curvatures and the demographic parameters of systems of Engel curves.     

Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions

We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (θ) and infinite dimensional unknown functions (h). Our proposal is to approximate h with a sieve and to estimate θ and the sieve parameters jointly by applying the method of minimum distance. We show that: (i) the sieve estimator of h is consistent with a rate faster than n^‐1/4 under certain metric; (ii) the estimator of θ is √n consistent and asymptotically normally distributed; (iii) the estimator for the asymptotic covariance of the θ estimator is consistent and easy to compute; and (iv) the optimally weighted minimum distance estimator of θ attains the semiparametric efficiency bound. We illustrate our results with two examples: a partially linear regression with an endogenous nonparametric part, and a partially additive IV regression with a link function.     

Conditionally unbiased estimation in the normal setting with unknown variances

To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes.     

Estimation of common location parameter of two exponential populations based on records

Consider the problem of estimating the common location parameter of two exponential populations using record data when the scale parameters are unknown. We derive the maximum likelihood estimator (MLE), the modified maximum likelihood estimator (MMLE) and the uniformly minimum variance unbiased estimator (UMVUE) of the common location parameter. Further, we derive a general result for inadmissibility of an equivariant estimator under the scaled-squared error loss function. Using this result, we conclude that the MLE and the UMVUE are inadmissible and better estimators are provided. A simulation study is conducted for comparing the performances of various competing estimators.     

Hazard ratio inference in stratified clinical trials with time‐to‐event endpoints and limited sample size

The stratified Cox model is commonly used for stratified clinical trials with time‐to‐event endpoints. The estimated log hazard ratio is approximately a weighted average of corresponding stratum‐specific Cox model estimates using inverse‐variance weights; the latter are optimal only under the (often implausible) assumption of a constant hazard ratio across strata. Focusing on trials with limited sample sizes (50‐200 subjects per treatment), we propose an alternative approach in which stratum‐specific estimates are obtained using a refined generalized logrank (RGLR) approach and then combined using either sample size or minimum risk weights for overall inference. Our proposal extends the work of Mehrotra et al, to incorporate the RGLR statistic, which outperforms the Cox model in the setting of proportional hazards and small samples. This work also entails development of a remarkably accurate plug‐in formula for the variance of RGLR‐based estimated log hazard ratios. We demonstrate using simulations that our proposed two‐step RGLR analysis delivers notably better results through smaller estimation bias and mean squared error and larger power than the stratified Cox model analysis when there is a treatment‐by‐stratum interaction, with similar performance when there is no interaction. Additionally, our method controls the type I error rate while the stratified Cox model does not in small samples. We illustrate our method using data from a clinical trial comparing two treatments for colon cancer.     

Student achievement against national minimum standards for reading and numeracy in Years 3, 5, 7 and 9: A regression discontinuity analysis

Literacy and numeracy are essential for success in life. In Australia, students’ academic achievement is monitored using the National Assessment Program – Literacy and Numeracy (NAPLAN). One of the aims of NAPLAN is that results will drive student improvement by identifying which students are underperforming against national minimum standards and who require additional learning support. This study investigated the performance trajectories of students who were identified as “below the national minimum standards”, relative to students above the national minimum standards. This study used standardised reading and numeracy test scores for Australian students and regression discontinuity methods to investigate whether students who performed below the national minimum standards showed improved performance in subsequent years compared with similar students above the national minimum standards threshold. On average, achievement gaps between students below the standards and students at or above the standards persisted across year levels. Labelling students below the national minimum standards had no significant effects on later reading or numeracy achievement. The results suggest the NAPLAN national minimum standards labelling is not driving student improvement, highlighting the need for effective early identification and interventions for children who do not meet school year benchmarks for literacy and numeracy.     

Developing a community-level anti-HIV/AIDS stigma and homophobia intervention in new York city: The project CHHANGE model

HIV/AIDS stigma and homophobia are associated with significant negative health and social outcomes among people living with HIV/AIDS (PLWHA) and those at risk of infection. Interventions to decrease HIV stigma have focused on providing information and education, changing attitudes and values, and increasing contact with people living with HIV/AIDS (PLWHA), activities that act to reduce stereotyped beliefs and prejudice, as well as acts of discrimination. Most anti-homophobia interventions have focused on bullying reduction and have been implemented at the secondary and post-secondary education levels. Few interventions address HIV stigma and homophobia and operate at the community level. Project CHHANGE, Challenge HIV Stigma and Homophobia and Gain Empowerment, was a community-level, multi-component anti-HIV/AIDS stigma and homophobia intervention designed to reduce HIV stigma and homophobia thus increasing access to HIV prevention and treatment access. The theory-based intervention included three primary components: workshops and trainings with local residents, businesses and community-based organizations (CBO); space-based events at a CBO-partner drop-in storefront and “pop-up” street-based events and outreach; and a bus shelter ad campaign. This paper describes the intervention design process, resultant intervention and the study team’s experiences working with the community. We conclude that CHHANGE was feasible and acceptable to the community. Promoting the labeling of gay and/or HIV-related “space” as a non-stigmatized, community resource, as well as providing opportunities for residents to have contact with targeted groups and to understand how HIV stigma and homophobia relate to HIV/AIDS prevalence in their neighborhood may be crucial components of successful anti-stigma and discrimination programming.