中国城镇职工健康及医疗服务需求的模型分析

健康是人生的一个重要方面 ,也是社会保障的一个重要指标。健康状况的改善对社会发展和经济增长的贡献是多方面的。在过去的几十年里 ,尽管世界人口的健康状况得到了改善 ,但是今后仍面临新的健康需求挑战 (ＴｈｅＷｏｒｌｄＢａｎｋ ,1993)。根据传统的消费需求理论 ,迈克·格罗斯曼(ＭｉｃｈａｅｌＧｒｏｓｓｍａｎ ,1972 )的理论认为 ,消费者对于健康的需求是出于以下两个原因 :其一 ,健康是一种消费商品 ,它可以使消费者感觉良好 ;其二 ,健康是一种投资商品 ,健康状况将决定消费者可利用的时间的多少。生病天数的减少将增加可用于工作和…     

The heterogeneous impact of insurance on health care demand among young adults: a panel data analysis*

Success of the recently implemented Affordable Care Act hinges on previously uninsured young adults enrolling in coverage. How will increased coverage, in turn, affect health care utilization? This paper applies variable coefficient panel models to estimate the impact of insurance on health care utilization among young adults. The econometric setup, which accommodates nonlinear usage measures, attempts to address the potential endogeneity of insurance status. The main finding is that, for approximately one-fifth of young adults, insurance does not substantially alter health care consumption. On the other hand, another one-fifth of young adults have large moral hazard effects. Among that group, insurance increases the probability of having a routine checkup by 71–120%, relative to mean probabilities, and insurance increases the number of curative-based doctor office visits by 67–181%, relative to the mean number of visits.     

A new discrete distribution: properties and applications in medical care

This paper proposes a simple and flexible count data regression model which is able to incorporate overdispersion (the variance is greater than the mean) and which can be considered a competitor to the Poisson model. As is well known, this classical model imposes the restriction that the conditional mean of each count variable must equal the conditional variance. Nevertheless, for the common case of well-dispersed counts the Poisson regression may not be appropriate, while the count regression model proposed here is potentially useful. We consider an application to model counts of medical care utilization by the elderly in the USA using a well-known data set from the National Medical Expenditure Survey (1987), where the dependent variable is the number of stays after hospital admission, and where 10 explanatory variables are analysed.     

A computationally efficient method for bootstrapping systems of demand equations: A comparison to traditional techniques

The solution to a Liapunov matrix equation (LME) has been proposed to estimate the parameters of the demand equations derived from the Translog, the Almost Ideal Demand System and the Rotterdam demand models. When compared to traditional scemingly unrelated regression (SUR) methods the LME approach saves both computer time and space, and it provides parameter estimates that are less likely to suffer from round-off error. However, the LME method is difficult to implement without the use of specially written computer programs and, unlike traditional SUR methods, it does not automatically provide an estimate of the covariance of the parameters. This paper solves these two problems, the first by providing a simplified solution to the Liapunov matrix equation which can be written in a few lines of code in computer languages such as SAS PROC MATRIX/IML^TM or GAUSS^TM; the second, by bootstrapping the parameter covariance matrix.     

An application of robust Bayesian analysis to a medical experiment

This paper reviews a design for a clinical trail that uses expert opinion to guide the selection of treatments for patients in a way intended to be more favorable than random selection. The problems of analyzing data from the design are discussed. Using real data from a trial of two agents for treating hypertension after open heart surgery, issues of how to display the data are considered, and the extent to which the design and analysis may be robust to elicitation error is discussed.     

The Geometric ArcTan distribution with applications to model demand for health services

In this paper , a new discrete two–parameter distribution α ∈ ? ? {0} and 0 < θ < 1, the Geometric ArcTan (GAT) distribution is introduced. The geometric distribution is a limiting case of this model when α tends to zero. Similarly to the the latter distribution, this probabilistic family is unimodal but the mode can be located at zero or in other point of the support. Then, after deriving some of its more relevant properties , the issue of parameter investigation is investigated. Next, the GAT distribution is used to explain the demand for health services by means of a regression model. Numerical results show that this new model outperforms the negative binomial distribution.     

Sensitivity analysis of longitudinal count responses: a local influence approach and application to medical data

Longitudinal count responses are often analyzed with a Poisson mixed model. However, under overdispersion, these responses are better described by a negative binomial mixed model. Estimators of the corresponding parameters are usually obtained by the maximum likelihood method. To investigate the stability of these maximum likelihood estimators, we propose a methodology of sensitivity analysis using local influence. As count responses are discrete, we are unable to perturb them with the standard scheme used in local influence. Then, we consider an appropriate perturbation for the means of these responses. The proposed methodology is useful in different applications, but particularly when medical data are analyzed, because the removal of influential cases can change the statistical results and then the medical decision. We study the performance of the methodology by using Monte Carlo simulation and applied it to real medical data related to epilepsy and headache. All of these numerical studies show the good performance and potential of the proposed methodology.     

The analysis of extreme pollution levels: A case study

Our case study focuses on Milan. Italian law specifies strict guidelines for the permissibility of high levels of a variety of air pollutants in cities. In Milan, a highly sophisticated network of recording stations has been constructed to monitor pollutant levels. The aim of this paper is to obtain a summary of the temporal behaviour of the pollutant series, with particular reference to extreme levels. Simple exploratory analysis reveals a number of sources of stochastic variation and possible dependence on covariate effects, which are subsequently modelled, exploiting recent developments in the modelling and inference for temporal extremes. Using this methodology, we examine the issues of data trends, non-stationarity, meteorological effects and temporal dependence, all of which have substantive implications in the design of pollution control regulations. Moreover, the asymptotic basis of these extreme value models justifies the interpretation of our results, even at levels that are exceptionally high.     

Automatic specification of piecewise linear additive models: application to forecasting natural gas demand

When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumptions and piecewise linear regression. This procedure allows the analyst to gain insight about the problem by examining the automatically selected model, thus easily checking the validity of the forecast. Monte Carlo simulations have been run to ensure that the model selection procedure behaves correctly under weakly dependent data. Moreover, comparison over other well-known methodologies has been done to evaluate its accuracy performance, both in simulated data and in the context of short-term natural gas demand forecasting. Empirical results show that the accuracy of the proposed model is competitive against more complex methods such as neural networks.     

A model-based approach to Spotify data analysis: a Beta GLMM

Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model, including random effects, is proposed in order to give the first answer to questions like: which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of characteristics of those considered most important in making a song popular is a very interesting topic for those who aim to predict the success of new products.     

A restricted Liu estimator for binary regression models and its application to an applied demand system

In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by Rβ?=?r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.     

A stochastic frontier approach to survival analysis

In spite of the best set of covariates and statistical tools for the survival analysis, there are instances when experts do not rule out the existence of many non-observable factors that could influence the survival probability of an individual. The fact that every human body, sick or otherwise, strives to maximize time to death, renders the stochastic frontier analysis (vide 2 Kumbhakar, S. C. and Lovell, C. A.K. 2003. Stochastic Frontier Analysis, Cambridge: Cambridge University Press.  [Google Scholar]) as a meaningful tool to measure the unobservable individual-specific deficiency factor that accounts for the difference between the optimal and observed survival times. In this paper, given the survival data, an attempt is made to measure the deficiency factor for each individual in the data on adopting the stochastic frontier analysis. Such an attempt to quantify the effect of these unobservable factors can provide ample scope for further research in bio-medical studies. The utility of these estimates in the survival analysis is also highlighted using a real-life data.     

A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi‐likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis.     

Marginal and hazard ratio specific random data generation: Applications to semi-parametric bootstrapping

Cox's partial likelihood for censored time-to-event data can be interpreted as a permutation probability, whereby covariate values are permuted to the observed times-to-event and censoring times. This interpretation facilitates a simple method for jointly generating times-to-event and covariate tuples with considerable flexibility, including time dependence of the hazard ratio and specification of both the marginal time-to-event and covariate distributions. This interpretation also facilitates a method for semi-parametric bootstrapping of hazard ratio estimators.     

The analysis of the AB/BA cross‐over trial in the medical literature

The evolution of opinion as to how to analyse the AB/BA cross‐over trials is described by examining the recommendations of three key papers. The impact of these papers on the medical literature is analysed by looking at citation rates as a function of various factors. It is concluded that amongst practitioners there is a highly imperfect appreciation of the issues raised by the possibility of carry‐over. Copyright © 2004 John Wiley & Sons, Ltd.     

The application of bias to discriminant analysis

When classification rules are constructed using sample estimatest it is known that the probability of misclassification is not minimized. This article introduces a biased minimum X² rule to classify items from a multivariate normal population. Using the principle of variance reduction, the probability of misclassification is reduced when the biased procedure is employed. Results of sampling experiments over a broad range of conditions are provided to demonstrate this improvement.