A Note on the Role of the Propensity Score for Estimating Average Treatment Effects

Hahn [Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66:315-331] derived the semiparametric efficiency bounds for estimating the average treatment effect (ATE) and the average treatment effect on the treated (ATET). The variance of ATET depends on whether the propensity score is known or unknown. Hahn attributes this to “dimension reduction.” In this paper, an alternative explanation is given: Knowledge of the propensity score improves upon the estimation of the distribution of the confounding variables.     

Median treatment effect in randomized trials

For two response variables y _t and y _c corresponding to two treatments for two policies) T and C , we wish to learn about quantiles of y _t− y _c from the marginal quantiles of y _t and y _c; only one of y _t and y _c is observed for an individual. We find that, in general, this is difficult for quantiles other than the median unless strong assumptions are imposed on how y _t is related to y _c. For the median, we present conditions under which the sign of the median treatment effect is identified.     

北宋书序作者所选择的传播因素分析

书序具有重要的传播作用,以北宋诗文集序为对象考察,书序作者在选择推动传播的因素时,对作品艺术性、知识性等文本因素的关注不如对作家身份、生平轶事等非文本因素普遍。其中原因,从书序作者的操作层面来看,是因为在书序中进行文艺批评是很难把握的;从面对读者的传播效果层面来分析,是因为非文本因素较之文本相关的评论因素可以产生显著的传播效果。于是,非文本因素在文学传播中变成了一位无形的操纵者,虽然它不是长远的传播之道,但却常常起着奇妙的传播作用,从而给书序这一文体增加活力。     

Impact of Hazardous Substances Regulations on Small Firms in Delaware and New Jersey

Regulations under the 1990 Amendments to the Clean Air Act impose significant requirements on firms handling hazardous chemicals. The nature of the regulations would suggest that small firms, because of limited resources and other structural limitations, would experience more difficulty complying with the regulations than large firms. To understand the difficulties imposed by the regulations on small firms, we interviewed ten small firms in Delaware and New Jersey, states with existing hazardous regulations similar to those being considered by the U.S. Environmental Protection Agency, and evaluated their responses to state regulations. The impacts of the environmental regulations on the firms and on the risk levels of their businesses are discussed. Propositions for research into small firms compliance are developed. Possible means for reducing the regulatory burden on small firms while enhancing regulatory effectiveness are suggested.     

Analyzing Binomial Data in a Split-Plot Design: Classical Approach or Modern Techniques?

Modeling data that are non-normally distributed with random effects is the major challenge in analyzing binomial data in split-plot designs. Seven methods for analyzing such data using mixed, generalized linear, or generalized linear mixed models are compared for the size and power of the tests. This study shows that analyzing random effects properly is more important than adjusting the analysis for non-normality. Methods based on mixed and generalized linear mixed models hold Type I error rates better than generalized linear models. Mixed model methods tend to have higher power than generalized linear mixed models when the sample size is small.     

Estimated Data Collection Organization Effect in the National Medical Care Expenditure Survey

The National Medical Care Expenditure Survey, which has a complex survey design, is further complicated by combining two independently drawn national samples of households from the Research Triangle Institute (RTI) and the National Opinion Research Center (NORC). It is assumed that since the structures of these national-area samples are similar, they are thereby compatible and allow for the derivation of statistically equivalent unbiased national estimates of relevant health parameters. In this paper, national parameter estimates of relevant demographic and health measures were separately produced for each of the two survey organizations and a determination was made regarding statistical equivalence across samples. In addition, the data-collection organization effect was estimated and tested within the framework of a multivariate analysis appropriate for complex survey data. The findings consistently revealed a statistically nonsignificant data-collection organization effect when testing for differences in domain estimates of the relevant health parameters under consideration.     

Resolving the Mixed Models Controversy

Two standard mixed models with interactions are discussed. When each is viewed in the context of superpopulation models, the mixed models controversy is resolved. The tests suggested by the expected mean squares under the constrained-parameters model are correct for testing the main effects and interactions under both the unconstrained-and constrained-parameters models.     

Fractional replication in simulation studies

It is shown how to use fractional replication in simulation studies. Examples are given. Considerable savings in number of runs required can be achieved through the use of fractional replication ideas.     

Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.     

MODELING THE LIFETIME OF LONGITUDINAL ELEMENTS

In the study of the stochastic behaviour of the lifetime of an element as a function of its length, it is often observed that the failure time (or lifetime) decreases as the length increases. In probabilistic terms, such an idea can be expressed as follows. Let T be the lifetime of a specimen of length x, so the survival function, which denotes the probability that an element of length x survives till time t, will be given by S_T (t, x) = P(T > t/α(x), where α(x) is a monotonically decreasing function. In particular, it is often assumed that T has a Weibull distribution. In this paper, we propose a generalization of this Weibull model by assuming that the distribution of T is Generalized gamma (GG). Since the GG model contains the Weibull, Gamma and Lognormal models as special and limiting cases, a GG regression model is an appropriate tool for describing the size effect on the lifetime and for selecting among the embedded models. Maximum likelihood estimates are obtained for the GG regression model with α(x) = cx^b . As a special case this provide an alternative to the usual approach to estimation for the GG distribution which involves reparametrization. Related parametric inference issues are addressed and illustrated using two experimental data sets. Some discussion of censored data is also provided.