Partial central subspace and sliced average variance estimation

Sliced average variance estimation is one of many methods for estimating the central subspace. It was shown to be more comprehensive than sliced inverse regression in the sense that it consistently estimates the central subspace under mild conditions while slice inverse regression may estimate only a proper subset of the central subspace. In this paper we extend this method to regressions with qualitative predictors. We also provide tests of dimension and a marginal coordinate hypothesis test. We apply the method to a data set concerning lakes infested by Eurasian Watermilfoil, and compare this new method to the partial inverse regression estimator.     

Nonparametric causal effects based on marginal structural models

A parametric marginal structural model (PMSM) approach to Causal Inference has been favored since the introduction of MSMs by Robins [1998a. Marginal structural models. In: 1997 Proceedings of the American Statistical Association. American Statistical Association, Alexandria, VA, pp. 1–10]. We propose an alternative, nonparametric MSM (NPMSM) approach that extends the definition of causal parameters of interest and causal effects. This approach is appealing in practice as it does not require correct specification of a parametric model but instead relies on a working model which can be willingly misspecified. We propose a methodology for longitudinal data to generate and estimate so-called NPMSM parameters describing so-called nonparametric causal effects and provide insight on how to interpret these parameters causally in practice. Results are illustrated with a point treatment simulation study. The proposed NPMSM approach to Causal Inference is compared to the more typical PMSM approach and we contribute to the general understanding of PMSM estimation by addressing the issue of PMSM misspecification.     

Modified Shewhart Charts for High Yield Processes

The conventional Shewhart p or np chart is not effective for monitoring a high yield process, a process in which the defect level is close to zero. An improved Shewhart np chart for monitoring high yield processes is proposed. A review of control charts for monitoring high yield processes is first given. The run length performance of the proposed Shewhart chart is then compared with other high yield control charts. A simple procedure for designing the chart for processes subjected to sampling or 100% continuous inspection is provided and this allows the chart to be implemented easily on the factory floor. The practical aspects of implementation of the Shewhart chart are discussed. An application of the Shewhart chart based on a real data set is demonstrated.     

边缘贫困群体的形成原因及扶助对策——以江苏省南京市为例

边缘贫困群体是一个新出现的困难群体.结合江苏省南京市的具体情况,分析了这一群体形成的主要原因,认为"低保"标准偏低、界定的单一化、就业不稳定以及政府缺乏专业工作人员,是这一群体形成的主要原因.探讨了针对边缘贫困群体的4点扶助对策,即推行"发展性社会政策"、实行差别救助、积极发挥社区援助功效以及强化非正式社会支持网络.     

Hidden Markov chains in generalized linear models

We show how the concept of hidden Markov model may be accommodated in a setting involving multiple sequences of observations. The resulting class of models allows for both interrelationships between different sequences and serial dependence within sequences. Missing values in the observation sequences may be handled in a straightforward manner. We also examine a group of methods, based upon the observed Fisher Information matrix, for estimating the covariance matrix of the parameter estimates. We illustrate the methods with both real and simulated data sets.     

A Bootstrap Approach for Testing Marginal Independence Between Two Categorical Variables When Subjects Have Repeated Responses

To assess independence in two-way contingency tables, the Pearson chi-square test or Fisher’s exact test are typically used. These tests assume that each subject contributes at most one count to only one table cell (e.g., sex versus blood type). In other situations, each subject may have more than one count contributing to the table and these counts may occur in different cells of the table. One may wish to test independence, adjusting for the within-subject correlation. We provide a simple nonparametric bootstrap approach and assess its performance through simulation studies. The method is illustrated on subjects with multiple mental health presentations to Emergency Departments.     

A Partially Linear Model Using a Gaussian Process Prior

A partially linear model is a semiparametric regression model that consists of parametric and nonparametric regression components in an additive form. In this article, we propose a partially linear model using a Gaussian process regression approach and consider statistical inference of the proposed model. Based on the proposed model, the estimation procedure is described by posterior distributions of the unknown parameters and model comparisons between parametric representation and semi- and nonparametric representation are explored. Empirical analysis of the proposed model is performed with synthetic data and real data applications.     

Rank covariance matrix estimation of a partially known covariance matrix

Classical multivariate methods are often based on the sample covariance matrix, which is very sensitive to outlying observations. One alternative to the covariance matrix is the affine equivariant rank covariance matrix (RCM) that has been studied in Visuri et al. [2003. Affine equivariant multivariate rank methods. J. Statist. Plann. Inference 114, 161–185]. In this article we assume that the covariance matrix is partially known and study how to estimate the corresponding RCM. We use the properties that the RCM is affine equivariant and that the RCM is proportional to the inverse of the regular covariance matrix, and hence reduce the problem of estimating the original RCM to estimating marginal rank covariance matrices. This is a great computational advantage when the dimension of the original data vector is large.     

A marginalized diffusion model for estimating age at first lower endoscopy use from current status data

Summary.  We propose an approach for estimating the age at first lower endoscopy examination from current status data that were collected via two series of cross-sectional surveys. To model the national probability of ever having a lower endoscopy examination, we incorporate birth cohort effects into a mixed influence diffusion model. We link a state-specific model to the national level diffusion model by using a marginalized modelling approach. In future research, results from our model will be used as microsimulation model inputs to estimate the contribution of endoscopy examinations to observed changes in colorectal cancer incidence and mortality.     

Multivariate tests comparing binomial probabilities, with application to safety studies for drugs

Summary.  In magazine advertisements for new drugs, it is common to see summary tables that compare the relative frequency of several side-effects for the drug and for a placebo, based on results from placebo-controlled clinical trials. The paper summarizes ways to conduct a global test of equality of the population proportions for the drug and the vector of population proportions for the placebo. For multivariate normal responses, the Hotelling T ²-test is a well-known method for testing equality of a vector of means for two independent samples. The tests in the paper are analogues of this test for vectors of binary responses. The likelihood ratio tests can be computationally intensive or have poor asymptotic performance. Simple quadratic forms comparing the two vectors provide alternative tests. Much better performance results from using a score-type version with a null-estimated covariance matrix than from the sample covariance matrix that applies with an ordinary Wald test. For either type of statistic, asymptotic inference is often inadequate, so we also present alternative, exact permutation tests. Follow-up inferences are also discussed, and our methods are applied to safety data from a phase II clinical trial.