Factors Affecting Children’s Adjustment to the Death of a Parent: The Social Work Professional’s Viewpoint

This study investigated factors affecting the adjustment of parentally bereaved children by reviewing existing literature and comparing it to observations made by professional social workers. Interviewees had between 1 1/2 and 22 years of experience in counseling bereaved children. The specific factors examined were age and sex of the bereaved child, circumstances of the death, and adjustment of remaining caregiver. Participants (n = 5) responded to an interview instrument specifically designed for this study which contained seven in-depth, open-ended questions. Participants’ replies were mostly in accordance with the literature, but also generated some new perspectives that had not been previously considered for this study and had not appeared in the literature. This study has implications for practice with parentally bereaved children and their surviving caregivers.Rebecca M. Hope, LCSW, Outpatient Clinician, New River Valley Community Services, 700 University City Boulevard, Blacksburg, VA 24060.Diane M. Hodge, Ph.D. is an Associate Professor of Social Work, Radford University, P.O. Box 6958, Radford, VA 24141; e-mail: dmhodge@radford.eduAddress correspondence to Rebecca M. Hope, LCSW, Outpatient Clinician, New River Valley Community Services, 700 University City Boulevard, Blacksburg, VA 24060; e-mail: rmhope@verizon.net     

Estimation of error probabilities in stochastic dominance

The process of using data to infer the existence of stochastic dominance is subject to sampling error. Kroll and Levy (1980), among others, have presented simulation results for several normal and lognormal distributions which show high error probabilities for a wide range of parameter values. This paper continues this line of research and uses simulation to estimate error probabilities. Distributions considered are a pair of normals and a pair of lognormals. Analysis of these distributions is made computationally feasible through theoretical results which reduce the number of parameters of the pair of distributions from four to two.     

Bargaining over waiting time in ultimatum game experiments

While preference-based explanations play an increasing role in economics and sociology, the accurate measurement of social preferences deserves more attention. Most laboratory experiments measure social preferences by studying the division of “a cake that nobody had to bake” (Güth and Kliemt, 2003). This article reports results of the first ultimatum game experiment with bargaining over waiting time. The experiment was created to avoid effects of windfall gains. In contrast to donated money, time is not endowed by the experimenter and implies a natural loss to subjects. This allows for a better measurement of the inherent conflict in the ultimatum game. We implemented three anonymity conditions; one baseline condition, one condition with anonymity among subjects and one double-blind condition in which the experimenter did not know the division of waiting time. While we expected to observe less other-regarding behavior in ultimatum game bargaining over time, our experimental results rather confirm previous ultimatum game experiments, in which people bargained over money. The modal offer was half of the waiting time and only one offer was rejected. Interestingly, anonymity did not change the results significantly. In conclusion, our experiment confirms other-regarding behavior in the ultimatum game.     

Statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data

In this article, the statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data is discussed. The estimation of the parameters for Gompertz distribution is obtained using maximum likelihood method (MLE) and Bayesian method under three different loss functions. We also proved the existence and uniqueness of the MLE. The one-sample Bayesian prediction intervals are obtained. The work is done for different values of the parameters. We apply the Monto Carlo simulation to compare the proposed methods, also an example is discussed to construct the Prediction intervals.     

Nonparametric estimation of welfare changes

In this article, I propose a simple nonparametric method of estimating welfare measures with no structural assumptions on the demand system. The method is illustrated for both the single good and the multiple good case, using simulated data. Standard errors of the welfare changes are derived using standard bootstrap techniques. The compensating variation and deadweight loss resulting from a housing price increase is evaluated using Canadian mortgage data. The GAUSS coding, which is very short, is included.     

A Bayesian discovery procedure

Summary.  We discuss a Bayesian discovery procedure for multiple-comparison problems. We show that, under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule that is based on a threshold of the posterior probability of the alternative. Under a semiparametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure, which was recently introduced by Storey. Improving the approximation leads us to a Bayesian discovery procedure, which exploits the multiple shrinkage in clusters that are implied by the assumed non-parametric model. We compare the Bayesian discovery procedure and the optimal discovery procedure estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumour samples. We extend the setting of the optimal discovery procedure by discussing modifications of the loss function that lead to different single-thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data.     

Global Robustness with Respect to the Loss Function and the Prior

We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of , we obtain the exact set.     

Procedures for improving the mle for ordered binomial parameters

The problem is that of estimating the probabilities of m independent binomial random variables when their probabilities are known to be nondecreasing and the loss function is squared error. In the cases where the m.l.e. is inadmissible (essentially when the total number of trials is 7 or more) we present a method for modifying the m.l.e. to get a better estimator. The method requires a series of changes. At each step we alter the action taken by the m.l.e. on each of three, appropriately chosen, points in the sample space.