Bayesian approach to estimation of intraclass correlation using reference prior

For the balanced variance component model when the intraclass correlation coefficient is of interest, Bayesian analysis is often appropriate. Berger and Bernardo’s (1992a) grouped ordering reference prior approach is used to analyze this model. The reference priors are developed and compared for the posterior inference with real and simulated data. We examine whether the reference priors satisfy the probability-matching criterion. Further, the reference prior is shown to be good in the sense of correct frequentist coverage probability of the posterior quantile.     

A Pilot for Improving Depression Care on College Campuses: Results of the College Breakthrough Series–Depression (CBS-D) Project

Abstract

Objective: To implement a pilot quality improvement project for depression identification and treatment in college health. Participants: Eight college health center teams composed primarily of primary care and counseling service directors and clinicians. Methods: Chronic (Collaborative) Care Model (CCM) used with standardized screening to identify, treat, and track depressed students for 12 weeks to monitor predetermined process and clinical outcomes. Results: Of all students receiving primary medical care services between January 2007 and May 2008, 69% (n = 71,908) were screened for depression. A total of 801 depressed students were treated and tracked; most predetermined treatment process and clinical outcome targets were achieved. Conclusion: The CCM for depression shows promise for improving depression identification and care for college students.     

A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function. Previous work has shown that rank-based estimators may be written as solutions to linear programming (LP) problems. However, the size of the LP problem is O(n ²+p) subject to n ² linear constraints, where n denotes sample size and p denotes the dimension of parameters. As n and/or p increases, the feasibility of such solution in practice becomes questionable. Among data mining and statistical learning enthusiasts, there is interest in extending ordinary regression coefficient estimators for low-dimensions into high-dimensional data mining tools through regularization. Applying this recipe to rank-based coefficient estimators leads to formidable optimization problems which may be avoided through smooth approximations to non-smooth functions. We review smooth approximations and quasi-Newton methods for rank-based estimation in AFT models. The computational cost of our method is substantially smaller than the corresponding LP problem and can be applied to small- or large-scale problems similarly. The algorithm described here allows one to couple rank-based estimation for censored data with virtually any regularization and is exemplified through four case studies.     

Bayesian estimation of P (Y<X) from Burr-type X model containing spurious observations

We consider the problem of estimating R=P(Y<X) when X and Y are independent Burr-type X random variables. We assume that the sample from each population contains one spurious observation. Bayes estimates are derived for exchangeable and identifiable cases. Monte Carlo simulation is carried out to compare the bias and the expected loss of R.     

The Opportunity Cost of Admission Preferences at Elite Universities*

Objective. This study examines how preferences for different types of applicants for admission to elite universities influence the number and composition of admitted students. Methods. Previous research with these NSCE data employed logistic regression analysis to link information on the admission decision for 124,374 applications to applicants' SAT scores, race, athletic ability, and legacy status, among other variables. Here we use micro simulations to illustrate what the effects might be if one were to withdraw preferences for different student groups. Results. Eliminating affirmative action would substantially reduce the share of African Americans and Hispanics among admitted students. Preferences for athletes and legacies, however, only mildly displace members of minority groups. Conclusions. Elite colleges and universities extend preferences to many types of students, yet affirmative action is the one most surrounded by controversy.     

Child Support Obligations and Low‐Income Fathers

Using the 1994–1998 waves of the Current Population Survey—Child Support Supplement (N =5,387), the aims of this study are to document child support obligation rates of nonresident fathers, to examine the effect of the obligation rate on child support compliance, and to calculate the trade‐off between fathers’ financial responsibility and children's well‐being, paying particular attention to low‐income fathers. The results indicate that low‐income fathers have high child support obligation rates, which significantly reduce their child support compliance. Although lowering the obligation rate for these fathers may improve their compliance, it does not fully offset the lowered obligation amounts and leads to a 30% net payment loss for welfare mothers and a 43% loss for nonwelfare mothers. Policy implications are discussed.     

The predictability of stock returns - a nonparametric approach

This paper reexamines the predictability of stock returns with a nonparametric model. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict one-month-ahead stock index returns with a nonparametric approach. We find that our nonparametricmodel. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict on -month-ahead stock index returns with a nonparametric approach. We find that our nonparametric model can correctly predict about 74% of stock index return signs. With various ex ante trading rules based on nonparametric predictions and transaction cost schedules, we then compare the performance of "managed" portfolios with that of the buy and hold portfolios. We fmd that the managed portfolios are mean-variance dominant over the buy-and-hold strategies when no or low transaction costs are assumed. When high transaction costs are assumed instead, the mean-variance dominance diminishes However,the Sharpe index of risk-adjusted portfolio performanceindicates that the managed portfolios significantly outperform the buy-and-hold strategies even for the high-transaction cost scenario. We show that the difference in performance between the managed portfolios and the buy-and-hold strategies can be partially explained by the January effect or the small firm effect. In sum, this paper demonstrates the merits of using a nonparametric approach for predicting stock returns and testing market efficiency.     

Family policy in Korea

In Korea, family policy as a public policy gained interest in the 1980s, when the Ministry of Health and Social Affairs (since January 1995 the Ministry of Health and Welfare) renamed the Maternal and Child Bureau the Family Welfare Bureau. This paper first presents a brief overview of the traditional family system and values in Korea and examines changes in economic and social indicators, changes in family functions and roles, and changes in attitudes toward family that have occurred following industrialization and urbanization. The paper then briefly explains some of the problems Korean families face today. It next examines the Family Planning Program and its results. Finally, it turns to an examination and critique of the policies and programs administered by the Family Welfare Bureau.Yeonoak Baik is a Family Advocacy Therapist for the 8th U.S. Army c/o Commander, 43rd Surgical Hospital (MA), ATTN: EAMC-SH-FAP, Room D-108, Unit #15190, APO AP, 96271-0126. She works in Korea with U.S. military soldiers and their dependents. Her research interests include child welfare, single-parent families, and domestic violence. Her MSW was received from the School of Social Work, University of Michigan, Ann Arbor.Her research interests include children of divorced families, social welfare policy, and social work administration in Korea. She received her Ph.D. in Social Welfare at Seoul National University in Korea.     

Scheduling Truck Arrivals at an Air Cargo Terminal

We consider the scheduling of truck arrivals at an air cargo terminal. By coordinating arrivals of cargo delivery trucks with outbound flight departure schedules, some of the shipments can be transferred directly to the departing flights, while others will be stored at the terminal's storage facility and incur extra handling and storage costs. The objective is to obtain a feasible schedule so as to minimize the total cost of operations. We formulate the problem as a time‐indexed integer program and show that, even with limited number of unloading docks at the terminal, the problem is non‐trivial (NP‐hard in the strong sense). Our solution method includes an exact solution procedure to determine an optimal unloading sequence for the shipments carried by each truck, together with a Lagrangian relaxation‐based heuristic for assigning trucks to truck docks and determining truck arrival times. We conducted computational experiments to test the performance of our solution method. Computational results show that our method can generate near‐optimal solutions efficiently. Our simulation results indicate that the scheduling approach proposed in this paper has the potential to generate significant cost savings over a first‐come, first‐served approach currently used at the air cargo terminal that we observed.     

Patterns of Advanced Manufacturing Technology Utilization and Manufacturing Capabilities

The objectives of this research are first to empirically replicate Advanced Manufacturing Technology (AMT) utilization taxonomies identified in foregoing research, second to investigate the relationship between patterns of AMT utilization and manufacturing capabilities attainment, and third to explore differences in context, and performance across AMT groups. Theories of performance frontiers and capability progression provide the basis for our hypotheses. Data were collected from 224 U.S. manufacturing plants in industries considered to have potential utilizations of AMTs. A cluster analysis of the data yields a solution that closely resembles a previous AMT utilization taxonomy, including four groups labeled, respectively, as Traditionalists, Generalists, High Investors, and Designers. Significant manufacturing capability differences across these four groups indicate that plants that utilize a broader scope of AMTs enjoy a greater breadth of manufacturing capabilities. The implied capability attainment pattern is consistent with cumulative capability theory. However, the results suggest that cost capability is not included in the capability mix when broad‐based AMT utilization is the enabler of capability gains. A post hoc exploration of the AMT groups indicates significant differences in performance across the groups. Collectively, the results extend prior research by providing added insights into the possible rationale and impact of AMT utilization patterns.