DEVELOPMENTAL LESSONS OF SEASONED MARITAL AND FAMILY THERAPISTS: A QUALITATIVE INVESTIGATION

Little has been published regarding the development of therapists during their professional careers. This qualitative research study explored the development of seasoned marriage and family therapists (MFTs) in order to identify important events and themes in their personal and professional lives. The main developmental theme that emerged was the integration of their personal and professional selves. Information gained from this study might be useful in understanding the self of the therapist within training and supervision and might partially provide a map of the possible transitions facing MFTs along their developmental journey.     

A diagnostic analysis of the impact of forecast errors on production planning via MRP system nervousness

Abstract. The purpose of this paper is to examine the impact of forecast errors on the performance of a multi-product, multilevel production planning system via MRP system nervousness. The accuracy of forecasting methods was at one time a major concern of production scheduling and inventory control. However, with the advent of material requirements planning (MRP) systems, the significance of selecting an accurate forecasting method has diminished. Inaccurate forecast results are taken as a fact of life in production planning. Instead of attempting to develop an accurate forecasting method, efforts have been devoted towards providing an appropriate buffering method ai the master production schedule level or on the shop floor level to counteract fluctuations in demand. MRP is capable of rescheduling planned orders as well as open orders to restore the priority integrity after the disruptive changes of forecast errors occur. Nevertheless, excessive rescheduling may lead to a problem, generally referred to as system nervousness. This study investigates this problem by means of a computer simulation model. The results show that the presence of forecasi     

Iterated Bootstrap-t Confidence Intervals for Density Functions

Abstract.  Conventional bootstrap- t intervals for density functions based on kernel density estimators exhibit poor coverages due to failure of the bootstrap to estimate the bias correctly. The problem can be resolved by either estimating the bias explicitly or undersmoothing the kernel density estimate to undermine its bias asymptotically. The resulting bias-corrected intervals have an optimal coverage error of order arbitrarily close to second order for a sufficiently smooth density function. We investigated the effects on coverage error of both bias-corrected intervals when the nominal coverage level is calibrated by the iterated bootstrap. In either case, an asymptotic reduction of coverage error is possible provided that the bias terms are handled using an extra round of smoothed bootstrapping. Under appropriate smoothness conditions, the optimal coverage error of the iterated bootstrap- t intervals has order arbitrarily close to third order. Examples of both simulated and real data are reported to illustrate the iterated bootstrap procedures.     

A SELF-MANAGEMENT MODEL FOR SUPERVISION

The authors present a management-of-self model of supervision that is currently being used in the graduate training program in marriage and family therapy at Virginia Polytechnic Institute and State University. The major proposition of the model is that as the supervisee comes to understand how family of origin and family constellation patterns learned in the past are reenacted within the therapeutic context, he/she can then interrupt those patterns of interaction that tend to inhibit his/her therapeutic effectiveness.     

Intrinsic Priors for Testing Two Normal Means with Intrinsic Bayes Factors

ABSTRACT

In this article we consider the problem of comparing two normal means with unknown common variance using a Bayesian approach. Conventional Bayes factors with improper non informative priors are not well defined. The intrinsic Bayes factors are used to overcome such a difficulty. We derive intrinsic priors whose Bayes factors are asymptotically equivalent to the corresponding intrinsic Bayes factors. We illustrate our results with numerical examples.