On the difficulty of making social choices

The difficulty of making social choices seems to take on two forms: one that is related to both preferences and the method used in aggregating them and one which is related to the preferences only. In the former type the difficulty has to do with the discrepancies of outcomes resulting from various preference aggregation methods and the computation of winners in elections. Some approaches and results which take their motivation from the computability theory are discussed. The latter institution-free type of difficulty pertains to solution theory of the voting games. We discuss the relationships between various solution concepts, e.g. uncovered set, Banks set, Copeland winners. Finally rough sets are utilized in an effort to measure the difficulty of making social choices.     

Program analysis using the client oriented cost outcome system.

The client oriented cost outcome system has been under development in Pennsylvania community and hospital programs since 1972. The system builds upon the behavioral and decision data generated with or on behalf of consumers. Flexibility for local program system design is permitted if the procedures of consumer intake, review, and termination document each consumer's (a) problems, resources, and goals, (b) overall functioning level in their ordinary community, and (c) services intended and rendered as related to (a), above. While the system's primary application is in providing feedback for local program quality assurance and evaluation procedures, aggregation of data permits program planning and evaluation at county, state, and federal levels in terms of (a) client demographic or diagnostic characteristics, and (b) program service characteristics and objectives.     

Involving constituents in agency priority setting: a case study

Many government agencies have "constituents" who believe the agencies should hear and consider their needs and desires in establishing program priorities. This article describes how one involves interested constituents (consumers, industry, medical professionals, and state organizations) in the agency's annual priority-setting process. This participation has produced a number of beneficial results-some expected and some unexpected.     

Personal income taxation under mobility, exogenous and endogenous welfare weights, and asymmetric information

This paper analyses how governments should tax labour income accruing to a group of highly skilled and geographically mobile individuals who divide their time or career between several jurisdictions. The analysis differs from previous models on migration and taxation by addressing optimal regulation when agents work for several principals. Optimal taxation is developed for social welfare functions with exogenous and endogenous welfare weights. Marginal income taxes are applied for screening purposes, and the rates are lower with endogenous than with exogenous welfare weights. Received: 22 January 1998/Accepted: 3 July 1999)     

数据挖掘中关联规则的规范化描述

一、引言数据挖掘(Data Mining)是近年来随着人工智能和数据库技术的发展而出现的一门新兴学科。它是从大量的数据中筛选出隐含的、可信的、新颖的、有效的信息的高级处理过程。关联规则(Association Rule)是其中重要的研究课题,是数据挖掘的主要技术之一,也是在无指导学习系统     

永久随机数在多层次调查中的应用

采用永久随机数法抽样技术抽到的样本具有很好地样本兼容的性质。本文讨论了采用序贯srswor抽样技术和Poisson抽样技术这两种永久随机数法抽样技术时,多层次调查的实现问题,希望能为多层次调查的实现提供一点启示。     

DENSITY ESTIMATION FOR CLUSTERED DATA

The commonly used survey technique of clustering introduces dependence into sample data. Such data is frequently used in economic analysis, though the dependence induced by the sample structure of the data is often ignored. In this paper, the effect of clustering on the non-parametric, kernel estimate of the density, f(x), is examined. The window width commonly used for density estimation for the case of i.i.d. data is shown to no longer be optimal. A new optimal bandwidth using a higher-order kernel is proposed and is shown to give a smaller integrated mean squared error than two window widths which are widely used for the case of i.i.d. data. Several illustrations from simulation are provided.     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Case–control studies with complex sampling

The use of complex sampling designs in population-based case–control studies is becoming more common, particularly for sampling the control population. This is prompted by all the usual cost and logistical benefits that are conferred by multistage sampling. Complex sampling has often been ignored in analysis but, with the advent of packages like SUDAAN, survey-weighted analyses that take account of the sample design can be carried out routinely. This paper explores this approach and more efficient alternatives, which can also be implemented by using readily available software.