A DISCRETE DYNAMIC OPTIMIZATION MODEL FOR MADE-TO-ORDER COST ANALYSIS

A model is presented that yields optimal production rates for a firm producing a contracted order. The model is unique in that it considers the influence of production rate and learning on total program cost. An application to the specific characteristics of two military production programs is presented. As demonstrated by the application, models of this type may be used as decision-making tools when negotiating the cost impact of contract modifications.     

FORECAST EVALUATION OF LIVINGSTON DATA ON INFLATION RATES AND INFLATION UNCERTAINTY*

Livingston data on expected inflation rates have been used extensively in the financial literature, especially in investgating the relationship between nominal interest rates and expected inflation rates; however, the data have not been subjected to a thorough analysis of forecast accuracy. The objective of this paper is to analyze the forecast properties of Livingston data using a wide variety of time and frequency domain methods over an extended sample period and for selected subperiods.     

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone.     

Die Kontaktgestaltung im Coaching

This article focusses on the many forms of the contact behaviour of systems. To this end, the gestalt theoretical concept of contact, and the system functions of the sociological system theory are combined to a new approach. In the second part of the article, this approach is transferred to the counseling format of coaching and ultimately made usable for coaches.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.     

Grey seals red in tooth and claw: how Darwin helps model their population

Grey seals in Britain are a conservation success, brought back from the brink of extinction by protection in the early 20th century. The public love them. Fishermen hate them. The resurgence of the "grey guzzlers" threatens their livelihood, they say. Len Thomas shows how Charles Darwin's ideas are used to determine seal numbers and population dynamics. Surprisingly, Darwin's theories have also influenced the statistical tools that are used.     

Confronting or Complementing? A Case Study on NGO–State Relations from Kerala, India

NGOs have, of late, found some of their traditional domains, such as provision of micro-credit and participatory development, coinciding with or being taken away by the state. How do they position themselves and retain relevance vis-a-vis the state in the changed scenario? Tracing the trajectory of interventions of a local NGO in Kerala, India, this article shows that NGOs exhibit ‘multiple identities’—selective collaboration, gap-filling and posing alternatives—in the process of engagement with the state. The ‘strategizing’ of such identities may hold the key to their relevance vis-a-vis the state.