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Judgment and decision making research overwhelmingly uses null hypothesis significance testing as the basis for statistical inference. This article examines an alternative, Bayesian approach which emphasizes the choice between two competing hypotheses and quantifies the balance of evidence provided by the data--one consequence of which is that experimental results may be taken to strongly favour the null hypothesis. We apply a recently-developed "Bayesian t-test" to existing studies of the anchoring effect in judgment, and examine how the change in approach affects both the tone of hypothesis testing and the substantive conclusions that one draws. We compare the Bayesian approach with Fisherian and Neyman-Pearson testing, examining its relationship to conventional p-values, the influence of effect size, and the importance of prior beliefs about the likely state of nature. The results give a sense of how Bayesian hypothesis testing might be applied to judgment and decision making research, and of both the advantages and challenges that a shift to this approach would entail. [ABSTRACT FROM AUTHOR]

We consider the scenario where additive, independent, and identically distributed (i.i.d) noise in an image is removed using an overcomplete set of linear transforms and thresholding. Rather than the standard approach, where one obtains the denoised signal by ad hoc averaging of the denoised estimates provided by denoising with each of the transforms, we formulate the optimal combination as a conditional linear estimation problem and solve it for optimal estimates. Our approach is independent of the utilized transforms and the thresholding scheme, and as we illustrate using oracle-based denoisers, it extends established work by exploiting a separate degree of freedom that is, in general, not reachable using previous techniques. Our derivation of the optimal estimates specifically relies on the assumption that the utilized transforms provide sparse decompositions. At the same time, our work is robust as it does not require any assumptions about image statistics beyond sparsity. Unlike existing work, which tries to devise ever more sophisticated transforms and thresholding algorithms to deal with the myriad types of image singularities, our work uses basic tools to obtain very high performance on singularities by taking better advantage of the sparsity that surrounds them. With well-established transforms, we obtain results that are competitive with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The crucial steps in a quantitative analysis of a decision problem are problem formulation, model building, analysis, and implementation. Given an initial model specification, the goal of analysis is to determine the values of the controllable or decision variables that optimize the objective function. Frequently the initial model is inadequate and must be reformulated. While modeling is an evolutionary process involving art and science, under certain conditions Response Surface Methodology (RSM) is an effective vehicle for constructing and parameterizing optimization models. RSM, which draws upon the areas of experimental design, modeling, inference, and optimization, utilizes different opening and ending strategies. Through simultaneous and sequential experimentation, the approximate region of the model's maximum response is found by employing the steepest ascent method. Subsequently, the exact values of the controllable variables that maximize the model's response are determined by canonical analysis. The RSM concepts are first developed within the context of a manufacturing problem. A potential application to simulation studies is then presented. [ABSTRACT FROM AUTHOR]

Personal, or subjective, probabilities are used as inputs to many inferential and decision-making models, and various procedures have been developed for the elicitation of such probabilities. Included among these elicitation procedures are scoring rules, which revolve the computation of a score based on the assessor's stated probabilities and on the event that actually occurs. The development of scoring rules has, in general, been restricted to the elicitation of discrete probability distributions. In this paper, families of scoring rules for the elicitation of continuous probability distributions are developed and discussed. [ABSTRACT FROM AUTHOR]

An analysis of variance approach is suggested for evaluating the impact of various salient interstudy differences on the results of comparable but not perfectly replicative studies. As an application, multivariate analysis of variance is used to assess differences in parameter estimates across 37 "quasi- replicative" studies based on the Fishbein Behavioral Intention Model. [ABSTRACT FROM AUTHOR]