On multiattributive risk aversion: some clarifying results

This article sheds some light on three concepts of risk aversion in a multiattributive decision framework introduced into the literature by Kihlstrom and Mirman (J Econ Theor 8:361–388, 1974), de Finetti (Giornale degli Economisti e Annali di Economia 11:685–709, 1952), Richard (Manage Sci 22:12–21, 1975), and Meyer (Preferences over time, New York, pp. 473–514, 1976). We review the three multiattributive risk aversion definitions as well as the notion of partial risk aversion, give a translation of these concepts into properties of the multiattributive utility function and reveal that Meyer’s, de Finetti/Richard’s concepts are very closely related. Moreover, it is shown that any additive utility function is risk neutral in the sense of de Finetti/Richard and Meyer independently of the risk attitude that it expresses in terms of Kihlstrom/Mirman. Additionally we introduce a multiattributive utility function derived from a one-dimensional function that leads to a coincidence of the differently defined risk attitudes.

Optimal partially replicated parallel-flats designs for 2×3 mixed factorial experiments

Augmenting additional repeated runs to an unreplicated factorial design provides an economical scheme of obtaining an unbiased estimate for the error variance based on pure replicates. The augmented partially replicated design usually performs satisfactorily in identifying truly active effects regardless of whether the effect sparsity principle holds. Liao and Chai (2009) proposed a set of sufficient conditions for a partially replicated two-level design to be D-optimal over the class of parallel-flats designs. In this article, we generalize their result to the 2^n₁×3^n₂ mixed factorial under D-, A- and E-optimality, and include the 2ⁿ and 3ⁿ symmetric factorials as special cases. In addition, some examples are given to illustrate the main results.     

Inference for proportional hazard model with propensity score

Since the publication of the seminal paper by Cox (1972), proportional hazard model has become very popular in regression analysis for right censored data. In observational studies, treatment assignment may depend on observed covariates. If these confounding variables are not accounted for properly, the inference based on the Cox proportional hazard model may perform poorly. As shown in Rosenbaum and Rubin (1983), under the strongly ignorable treatment assignment assumption, conditioning on the propensity score yields valid causal effect estimates. Therefore we incorporate the propensity score into the Cox model for causal inference with survival data. We derive the asymptotic property of the maximum partial likelihood estimator when the model is correctly specified. Simulation results show that our method performs quite well for observational data. The approach is applied to a real dataset on the time of readmission of trauma patients. We also derive the asymptotic property of the maximum partial likelihood estimator with a robust variance estimator, when the model is incorrectly specified.     

Measuring efficiency with products,by-products and parent-offspring relations: A conditional two-stage DEA model

In the usual data envelopment analysis (DEA) setting, as pioneered by Charnes et al. (1978) [1], it is assumed that a set of decision making units (DMUs) is to be evaluated in terms of their relative efficiencies in converting a bundle of inputs into a bundle of outputs. The usual assumption in DEA is that each output is impacted by each and every member of the input set. One particular area of recent research is that relating to partial input to output impacts where the main issue addressed is that in many settings not all inputs impact all outputs. In that situation the authors view the DMU as consisting of a set of mutually exclusive subunits, with each subunit having its own unique bundle of inputs and outputs. Examined as well in this area, is the presence of multiple processes for generating sets of outputs. Missing from that earlier work is consideration of the presence of outputs in the form of by-products, giving rise to a parent-offspring phenomenon. One of the modeling complications there is that the parent assumes two different roles; as an input affecting the offspring, while at the same time being the dominant output. This gives rise to a model that we refer to as conditional two-stage. Another complication is that in the presence of multiple processes, by-products often arise out of only a subset of those processes. In the current paper we develop a DEA-type of methodology to handle partial input to output impacts in the presence of by-products.     

Partial orders with respect to continuous covariates and tests for the proportional hazards model

Several omnibus tests of the proportional hazards assumption have been proposed in the literature. In the two-sample case, tests have also been developed against ordered alternatives like monotone hazard ratio and monotone ratio of cumulative hazards. Here we propose a natural extension of these partial orders to the case of continuous and potentially time varying covariates, and develop tests for the proportional hazards assumption against such ordered alternatives. The work is motivated by applications in biomedicine and economics where covariate effects often decay over lifetime. The proposed tests do not make restrictive assumptions on the underlying regression model, and are applicable in the presence of time varying covariates, multiple covariates and frailty. Small sample performance and an application to real data highlight the use of the framework and methodology to identify and model the nature of departures from proportionality.     

我国商业银行客户忠诚度研究

随着银行业竞争的日益激烈,提高客户忠诚度已经成为决定银行盈利能力的关键。本文通过分析影响我国银行客户忠诚度的因素,建立了客户忠诚度及其影响因素之间的结构方程模型,并采用PLS方法对模型的参数进行估计。在此基础上,基于某商业银行的实际调查数据给出了一个算例,对计算的结果进行了分析,并提出了相应的对策建议。     

用偏导数法求解一类二元函数方程

给出了形如f（w（xy））=R（f（x）f（y））的二元函数方程可微解的偏导数求法，将此类二元函数方程的求解归结为一阶常微分方程的求解问题     

一种颇具特色的振动模型

结合国产 CDP—IIA 型电子皮带秤的机械系统,介绍一种十字弹簧单支承双悬臂多质点梁的线性振动模型,在一般教科书上尚属罕见。其振动研究为电子皮带秤的灵敏性、稳定性和可靠性分析提供了科学依据。     

Comparison of the Polar Decision Rules for Various Types of Distributions

We focus on the dichotomous choice model, which goes back as far as Condorcet (1785; Essai sur l'application de l'analyse a la probabilité des décisions rendues a la pluralité des voix, Paris). A group of experts is required to select one of two alternatives, of which exactly one is regarded as correct. The alternatives may be related to a wide variety of areas. A decision rule translates the individual opinions of the members into a group decision. A decision rule is optimal if it maximizes the probability of the group to make a correct choice. In this paper we assume the correctness probabilities of the experts to be independent random variables, selected from some given distribution. Moreover, the ranking of the members in the team is (at least partly) known. Thus, one can follow rules based on this ranking. The polar different rules are the expert and the majority rules. The probabilities of the two polar rules being optimal were compared in a series of papers. The main purpose of this paper is to outline the results, providing exact formulas or estimates for these probabilities. We consider a variety of distributions and show that for all of these distributions the asymptotic behaviour of the probabilities of the two polar rules follows the same patterns.     

On the foundations of decision making under partial information

We show how to model incompleteness in the decision maker's judgements, within a Bayesian context, providing axioms which lead us to work with families of values or probabilities and utilities. The proper solution concepts are suggested. On the whole, we provide a more robust decision theory, based on a weaker set of axioms, but embodying coherence, since it essentially implies carrying out a family of coherent decision analyses.