Semiparametric estimation for count data through weighted distributions

This paper is concerned with semiparametric discrete kernel estimators when the unknown count distribution can be considered to have a general weighted Poisson form. The estimator is constructed by multiplying the Poisson estimate with a nonparametric discrete kernel-type estimate of the Poisson weight function. Comparisons are then carried out with the ordinary discrete kernel probability mass function estimators. The Poisson weight function is thus a local multiplicative correction factor, and is considered as the uniform measure to detect departures from the equidispersed Poisson distribution. In this way, the effects of dispersion and zero-proportion with respect to the standard Poisson distribution are also minimized. This method of estimation is also applied to the weighted binomial form for the count distribution having a finite support. The proposed estimators, in addition to being simple, easy-to-implement and effective, also outperform the competing nonparametric and parametric estimators in finite-sample situations. Two examples illustrate this new semiparametric estimation.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

Accelerated test system strength models based on Birnbaum–Saunders distribution: a complete Bayesian analysis and comparison

Several models for studies related to tensile strength of materials are proposed in the literature where the size or length component has been taken to be an important factor for studying the specimens’ failure behaviour. An important model, developed on the basis of cumulative damage approach, is the three-parameter extension of the Birnbaum–Saunders fatigue model that incorporates size of the specimen as an additional variable. This model is a strong competitor of the commonly used Weibull model and stands better than the traditional models, which do not incorporate the size effect. The paper considers two such cumulative damage models, checks their compatibility with a real dataset, compares them with some of the recent toolkits, and finally recommends a model, which appears an appropriate one. Throughout the study is Bayesian based on Markov chain Monte Carlo simulation.     

Analyzing the dynamic system model with discrete failure time distribution

The present study deals with the method of estimation of the parameters of k-components load-sharing parallel system model in which each component’s failure time distribution is assumed to be geometric. The maximum likelihood estimates of the load-share parameters with their standard errors are obtained. (1 − γ) 100% joint, Bonferroni simultaneous and two bootstrap confidence intervals for the parameters have been constructed. Further, recognizing the fact that life testing experiments are time consuming, it seems realistic to consider the load-share parameters to be random variable. Therefore, Bayes estimates along with their standard errors of the parameters are obtained by assuming Jeffrey’s invariant and gamma priors for the unknown parameters. Since, Bayes estimators can not be found in closed form expressions, Tierney and Kadane’s approximation method have been used to compute Bayes estimates and standard errors of the parameters. Markov Chain Monte Carlo technique such as Gibbs sampler is also used to obtain Bayes estimates and highest posterior density credible intervals of the load-share parameters. Metropolis–Hastings algorithm is used to generate samples from the posterior distributions of the unknown parameters.     

A new nonparametric bivariate test for two sample location problem

A strictly nonparametric bivariate test for two sample location problem is proposed. The proposed test is easy to apply and does not require the stringent condition of affine-symmetry or elliptical symmetry which is required by some of the major tests available for the same problem. The power function of the proposed test is calculated. The asymptotic distribution of the proposed test statistic is found to be normal. The power of proposed test is compared with some of the well-known tests under various distributions using Monte Carlo simulation technique. The power study shows that the proposed test statistic performs better than most of the test statistics for almost all the distributions considered here. As soon as the underlying population structure deviates from normality, the ability of the proposed test statistic to detect the smallest shift in location increases as compared to its competitors. The application of the test is shown by using a data set.     

Mortality attributable to obesity among middle-aged adults in the united states

Obesity is considered a major cause of premature mortality and a potential threat to the longstanding secular decline in mortality in the United States. We measure relative and attributable risks associated with obesity among middle-aged adults using data from the Health and Retirement Study (1992–2004). Although class II/III obesity (BMI _ 35.0 kg/m2) increases mortality by 40% in females and 62% in males compared with normal BMI (BMI = 18.5-24.9), class I obesity (BMI = 30.0-34.9) and being overweight (BMI = 25.0-29.9) are not associated with excess mortality. With respect to attributable mortality, class II/III obesity (BMI _ 35.0) is responsible for approximately 4% of deaths among females and 3% of deaths among males. Obesity is often compared with cigarette smoking as a major source of avoidable mortality. Smoking-attributable mortality is much larger in this cohort: about 36% in females and 50% in males. Results are robust to confounding by preexisting diseases, multiple dimensions of socioeconomic status (SES), smoking, and other correlates. These findings challenge the viewpoint that obesity will stem the long-term secular decline in U.S. mortality.     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.     

Quality_Family Deployment: A New Perspective in Determining Priority Importance for Improving Work Performance in Organization

Determining priority importance is a matter of concerns among the organization to improve their performance. One of the important aspects that should be considered by the organization is management of human resources, comprising of members who have their own family life. In this paper, we deliver a new perspective for organization to provide priority importance for their members with respect to family matters in order to inculcate the sense of belonging in the organization. To be effective in considering family matters in the organizational policy making, closer look of family characteristics are required. The idea of translating several family characteristics as quality variables and applying the quality function deployment (QFD) method to these variables could present a new way of improving the decision making in the organization by considering the process of decision in the family. Quality function deployment for family (QFDF) produces a friendly interpretation of a highly complex and intangible matters around family life; thus, making the assessment of a family easier. QFD is applied on the data gathered from a questionnaire survey based on 1,213 families in West Malaysia, Malaysia. Twelve variables are identified as voice of family, where seven of them are found to have the highest priority of importance. These variables could also be translated into quality variables in the context of organization, the strategy of improvement for the family could be interpreted as strategy for improvement in the organization. Furthermore, the results of this study provide suggestion to improve actions for managing human resource. In the discussion, three most high ranked variables from both perspectives of family and organization are considered.     

复活的记忆——却西德哇传统村运会的应用人类学研究

"阿米嘉顶之韵——却西德哇传统村运会"是由人类学者发起的一项旨在复活当地传统体育游戏、歌舞和其他古老传统的应用人类学项目。本文描述了面对传统文化的衰落,人类学者在青藏高原东北部一个名为却西德哇的藏族村落与村民对话和共同行动的过程。此外,本文还就本次实践在非物质文化遗产保护方面取得的经验做了讨论。     

Do Some Students Need Special Protection From Research on Sex and Trauma? New Evidence for Young Adult Resilience in “Sensitive Topics” Research

Institutional review boards (IRBs) have expressed concerns that certain individuals or groups, such as participants who are younger, ethnic minorities, or who have certain psychological or personality traits, may be particularly distressed when participating in “sensitive topics” research. This study examined the effects of several demographic and individual difference factors (i.e., age, sex, ethnicity, religiosity, Big Five personality traits, and baseline psychological distress levels) on reactions to participation in sensitive topics research. Participants were 504 undergraduates who completed an extensive battery of either trauma/sex questionnaires or cognitive tests and rated their positive and negative emotional reactions and the perceived benefits and mental costs of participating. They also compared research participation to normal life stressors. Our findings indicated that individual difference and demographic risk factors do not increase participant distress after participating in sex/trauma research over and above that experienced after participating in traditionally minimal-risk cognitive tasks. Participants generally found research participation less distressing than normal life stressors and even enjoyable.