ESTIMATION AND TESTING FOR PARTIALLY LINEAR SINGLE-INDEX MODELS

In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select variables and estimate regression coefficients. We show that the resulting SCAD estimators are consistent and possess the oracle property. Subsequently, we demonstrate that a proposed tuning parameter selector, BIC, identifies the true model consistently. Finally, we develop a linear hypothesis test for the parametric coefficients and a goodness-of-fit test for the nonparametric component, respectively. Monte Carlo studies are also presented.     

A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION

By providing new insights into the distribution of a protein's torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is "sparse" in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.     

Assessing inter- and intra-agreement for dependent binary data: a Bayesian hierarchical correlation approach

Agreement measures are designed to assess consistency between different instruments rating measurements of interest. When the individual responses are correlated with multilevel structure of nestings and clusters, traditional approaches are not readily available to estimate the inter- and intra-agreement for such complex multilevel settings. Our research stems from conformity evaluation between optometric devices with measurements on both eyes, equality tests of agreement in high myopic status between monozygous twins and dizygous twins, and assessment of reliability for different pathologists in dysplasia. In this paper, we focus on applying a Bayesian hierarchical correlation model incorporating adjustment for explanatory variables and nesting correlation structures to assess the inter- and intra-agreement through correlations of random effects for various sources. This Bayesian generalized linear mixed-effects model (GLMM) is further compared with the approximate intra-class correlation coefficients and kappa measures by the traditional Cohen’s kappa statistic and the generalized estimating equations (GEE) approach. The results of comparison studies reveal that the Bayesian GLMM provides a reliable and stable procedure in estimating inter- and intra-agreement simultaneously after adjusting for covariates and correlation structures, in marked contrast to Cohen’s kappa and the GEE approach.     

An Estimator of the Survival Function Basedon the Semi-Markov Model Under Dependent Censorship

Lee and Wolfe (Biometrics vol. 54 pp. 1176–1178, 1998) proposed the two-stage sampling design for testing the assumption of independent censoring, which involves further follow-up of a subset of lost-to-follow-up censored subjects. They also proposed an adjusted estimator for the survivor function for a proportional hazards model under the dependent censoring model. In this paper, a new estimator for the survivor function is proposed for the semi-Markov model under the dependent censorship on the basis of the two-stage sampling data. The consistency and the asymptotic distribution of the proposed estimator are derived. The estimation procedure is illustrated with an example of lung cancer clinical trial and simulation results are reported of the mean squared errors of estimators under a proportional hazards and two different nonproportional hazards models.     

Foundational issues concerning the analysis of censored data

The common approach to analyzing censored data utilizes competing risk models; a class of distribution is first chosen and then the sufficient statistics are identified! An operational Bayesian approach (Barlow 1993) for analyzing censored data would require a somewhat different methodology. In this approach, we first determine potentially observable parameters of interest. We then determine the data summaries (sufficient statistics) for these parameters. Tsai (1994) suggests that the observed sample frequency is sufficient for predicting the population frequency. Invariant probability measures (likelihoods), conditional on the parameters of interest, are then derived based on the principle of sufficiency and the principle of insufficient reason.Research partially supported by the Army Research Office (DAAL03-91-G-0046) grant to the University of California at Berkeley.     

Voluntary and nonvoluntary childlessness: personality vs. structural implications

"A 'deviant behavior' interpretation of childlessness has led many researchers to view this phenomenon as psychosocial in nature. Married persons who decide to remain childless are seen as having a unique set of personality and behavioral characteristics that go beyond the simple decision not to have children. An alternative explanation for these differences could center around the structural factor of childlessness per se, in that a family with one or more children may be a totally different social system than the dyadic-type relationships which comprise the childless couple. This paper reports research findings from an empirical test of the extent to which a national sample of [U.S.] respondents who are members of voluntary or nonvoluntary childless couples differ in terms of 12 psychosocial characteristics selected either on the basis of past research findings, or predicted on the basis of these findings....Findings showed that the two groups of respondents did strongly differ, but in ways opposite to those predicted on the basis of past research. An argument is presented that these strong, but contrary findings might be better explained by structural, as opposed to psychosocial, factors."     

Modeling data with a truncated and inflated Poisson distribution

Zero inflated Poisson regression is a model commonly used to analyze data with excessive zeros. Although many models have been developed to fit zero-inflated data, most of them strongly depend on the special features of the individual data. For example, there is a need for new models when dealing with truncated and inflated data. In this paper, we propose a new model that is sufficiently flexible to model inflation and truncation simultaneously, and which is a mixture of a multinomial logistic and a truncated Poisson regression, in which the multinomial logistic component models the occurrence of excessive counts. The truncated Poisson regression models the counts that are assumed to follow a truncated Poisson distribution. The performance of our proposed model is evaluated through simulation studies, and our model is found to have the smallest mean absolute error and best model fit. In the empirical example, the data are truncated with inflated values of zero and fourteen, and the results show that our model has a better fit than the other competing models.     

Experiences of trafficked and sexually exploited boys transitioning from shelter programmes into the community: Findings from a longitudinal study

This article explores the perspectives of Cambodian boys who have experienced human trafficking and sexual exploitation on their experiences transitioning out of shelters and re‐entering the community. We used an interpretive phenomenological approach to analyse 81 interviews and narrative summaries of interviews drawn from Chab Dai's 10‐year longitudinal study with survivors in Cambodia (n = 22). Themes included: minimal involvement in planning for re/integration; conflicted feelings about life in the community; challenges completing school and securing employment; importance of community‐based services; unfulfilled expectations; violence in the community; and a desire to return to the shelter.     

Mis-specification analyses of gamma and Wiener degradation processes

Degradation models are widely used these days to assess the lifetime information of highly reliable products if there exist some quality characteristics (QC) whose degradation over time can be related to the reliability of the product. In this study, motivated by a laser data, we investigate the mis-specification effect on the prediction of product's MTTF (mean-time-to-failure) when the degradation model is wrongly fitted. More specifically, we derive an expression for the asymptotic distribution of quasi-MLE (QMLE) of the product's MTTF when the true model comes from gamma degradation process, but is wrongly assumed to be Wiener degradation process. The penalty for the model mis-specification can then be addressed sequentially. The result demonstrates that the effect on the accuracy of the product's MTTF prediction strongly depends on the ratio of critical value to the scale parameter of the gamma degradation process. The effects on the precision of the product's MTTF prediction are observed to be serious when the shape and scale parameters of the gamma degradation process are large. We then carry out a simulation study to evaluate the penalty of the model mis-specification, using which we show that the simulation results are quite close to the theoretical ones even when the sample size and termination time are not large. For the reverse mis-specification problem, i.e., when the true degradation is a Wiener process, but is wrongly assumed to be a gamma degradation process, we carry out a Monte Carlo simulation study to examine the effect of the corresponding model mis-specification. The obtained results reveal that the effect of this model mis-specification is negligible.