Interval censored regression with fixed effects

This paper considers identification and estimation of a fixed-effects model with an interval-censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross-sectional units as long as they are stationary within a cross-sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional-logit-type composite likelihood estimator is proposed for the logistic fixed-effects model, and a composite maximum-score-type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data-coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.     

Two–step sequential sampling

Choosing the sample size in advance is a familiar problem: often, additional observations appear to be desirable. The final sample size then becomes a random variable, which has rather serious consequences.
Two such sample extension situations will be considered here. In the first situation, the observed sample variance determines whether or not to double the original sample size. In the second situation, the variances observed in two independent samples are compared; their ratio determines the number of additional observations.     

Contextual Determinants of Variable Pay Plan Design: A Proposed Research Framework

Increasingly, organizations are using variable pay plans to reward employees for the results that they achieve. Current discussion of variable pay focuses on variable pay plan design mechanics, with insufficient attention given to contextual variables that may affect variable pay plan design. We offer a preliminary framework for examining the contextual determinants of variable pay plan design. Components of the framework include characteristics of the environment, characteristics of the organization, and the organization's pay strategies. Propositions for future research are offered.     

Adjustments and their Consequences—Collapsibility Analysis using Graphical Models

We review probabilistic and graphical rules for detecting situations in which a dependence of one variable on another is altered by adjusting for a third variable (i.e., non‐collapsibility or non‐invariance under adjustment), whether that dependence is causal or purely predictive. We focus on distinguishing situations in which adjustment will reduce, increase, or leave unchanged the degree of bias in an association that is taken to represent a causal effect of one variable on the other. We then consider situations in which adjustment may partially remove or introduce a bias in estimating causal effects, and some additional special cases useful for case‐control studies, cohort studies with loss, and trials with non‐compliance (non‐adherence).     

Finite maturity caps and floors on continuous flows

Models of interest rate caps and floors are typically based on discrete rates over finite horizons while existing real option models describe perpetual claims on the maximum of two continuous flows. In this paper, we produce formulae for finite maturity caps and floors that are contingent on continuous flows. We present hedge ratios and discuss applications where a lognormally distributed flow variable is suitable. For other situations where practitioners use proprietary models, the formula presented is useful as a quick, tractable and universal means for mapping quoted implied to prices and vice versa.     

建筑工程常见渗漏原因与质量控制

外墙渗漏、屋面渗漏、厨房、卫生间渗漏至今仍是建筑工程的防治重点。造成建筑渗漏的原因错综复杂,涉及工程设计、施工到使用的每个环节,在实践中应根据不同原因,采取相应的预防措施,以避免或减少建筑渗漏问题的发生。     

On the analysis of multivariate growth curves

Growth curve data arise when repeated measurements are observed on a number of individuals with an ordered dimension for occasions. Such data appear frequently in almost all fields in which statistical models are used, for instance in medicine, agriculture and engineering. In medicine, for example, more than one variable is often measured on each occasion. However, analyses are usually based on exploration of repeated measurements of only one variable. The consequence is that the information contained in the between-variables correlation structure will be discarded.  In this study we propose a multivariate model based on the random coefficient regression model for the analysis of growth curve data. Closed-form expressions for the model parameters are derived under the maximum likelihood (ML) and the restricted maximum likelihood (REML) framework. It is shown that in certain situations estimated variances of growth curve parameters are greater for REML. Also a method is proposed for testing general linear hypotheses. One numerical example is provided to illustrate the methods discussed. Received: 22 February 1999     

Beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood

We propose a beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood. The proposed method is useful for those situations where the response variable is a rate or a proportion. An approach to the spatial generalized linear mixed models using the Box–Cox transformation in the precision model is presented. Thus, the parameter optimization process is developed for both the spatial mean model and the spatial variable dispersion model. All the parameters are estimated using Markov chain Monte Carlo maximum likelihood. Statistical inference over the parameters is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Diagnosis and prediction of a new observation are also developed. The method is illustrated with the analysis of one simulated case and two studies: clay and magnesium contents. In the clay study, 147 soil profile observations were taken from the research area of the Tropenbos Cameroon Programme, with explanatory variables: elevation in metres above sea level, agro‐ecological zone, reference soil group and land cover type. In the magnesium content, the soil samples were taken from 0‐ to 20‐cm‐depth layer at each of the 178 locations, and the response variable is related to the spatial locations, altitude and sub‐region.     

A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification

We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on simulated datasets and two real-world datasets.     

机械制造工程学课程设计教材体系探索

本文分析了机械制造工程学课程设计的新形式,对课程设计教材编写方面提出了一些改革措施,对于提高课程设计质量,推动创新性人才培养具有一定的作用。     

基于工作过程的高职《服装生产设备》课程改革

《服装生产设备》课程是高职服装设计专业学生的一门专业基础课。以典型任务为基础来构建相应的知识与技能,创建职业情境,理实一体化的教学模式,采用多样化的考评形式来评价学生,在工学结合中培养学生的职业能力。     

State‐Dependent Threshold Smooth Transition Autoregressive Models*

In this article, we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State‐dependent contemporaneous‐threshold STAR and logistic STAR models are introduced and discussed. These models are also used to investigate the dynamics of US short‐term interest rates, where the threshold is allowed to be a function of past output growth and inflation.     

Gamma-Minimax Prediction for the Multinomial Distribution

Characterizations of gamma-minimax predictors for the linear combinations of the unknown parameter and the random variable having the multinomial distribution under arbitrary squared error loss are established in two situations – when the sample size is fixed and when the sample size is a realization of a random variable. It is always assumed that the available vague prior information about the unknown parameter can be described by a class of priors whose vector of first moments belongs to a suitable convex and compact set. Several known gamma-minimax and minimax results can be obtained from the characterizations derived in the present paper.     

STOCHASTIC INTEGER PROGRAMMING BY DYNAMIC PROGRAMMING

Stochastic integer programming is a suitable tool for modeling hierarchical decision situations with combinatorial features. In continuation of our work on the design and analysis of heuristics for such problems, we now try to find optimal solutions. Dynamic programming techniques can be used to exploit the structure of two–stage scheduling, bin packing and multiknapsack problems. Numerical results for small instances of these problems are presented.     

Simultaneous dependence between firm-level stock returns

We show that use of ordinary least-squares to explore relationships involving firm-level stock returns as the dependent variable in the face of structured dependence between individual firms leads to an endogeneity problem. This in turn leads to biased and inconsistent least-squares estimates. A maximum likelihood estimation procedure that will produce consistent estimates in these situations is illustrated. This is done using methods that have been developed to deal with spatial dependence between regional data observations, which can be applied to situations involving firm-level observations that exhibit a structure of dependence. In addition, we show how to correctly interpret maximum likelihood parameter estimates from these models in the context of firm-level dependence, and provide a Monte Carlo as well as applied illustration of the magnitude of bias that can arise.     

Model assisted survey sampling strategy in two phases

Postulating a super-population regression model connecting a size variable, a cheaply measurable variable and an expensively observable variable of interest, an asymptotically optimal double sampling strategy to estimate the survey population total of the third variable is specified. To render it practicable, unknown model-parameters in the optimal estimator are replaced by appropriate statistics. The resulting generalized regression estimator is then shown to have a model-cum-asymptotic design based expected square error equal to that of the asymptotically optimum estimator itself. An estimator for design variance of the estimator is also proposed.     

宽带薪酬体系探讨

宽带薪酬,是近几年兴起的一种与现代组织结构扁平化和以能力绩效为导向的,管理模式相适应的新型薪酬体系。宽带薪酬与传统薪酬结构相比,强调的不再是岗位等级结构而是更注重员工的能力与绩效。但是宽带薪酬的设计和运用也对组织提出了更高的要求,因此企业要根据实际情况理性地选择与实施。     

SUV车变速操纵机构隔热方案优化设计

变速操纵机构是影响整车操纵舒适性能的重要机构,因此,变速操纵机构的合理化设计是至关重要的。文章通过SUV车在道路试验中出现变速操纵钢索被烤坏的现象,详细介绍了变速操纵机构隔热方案的优化设计过程,并通过试验验证了本次设计可以有效保护变速操纵机构。本次设计不仅解决了变速操纵钢索被发动机排气管烤坏的问题,填补了SUV车变速操纵机构隔热方案设计的空白,为SUV车隔热方案设计积累了宝贵经验,同时也提高了SUV车产品质量。     

On the construction of trend free row-column 2-level factorial experiments

In this paper we consider experimental situations where a complete or fractional factorial experiment having all factors at 2 levels is to be conducted using a 2 ^m × 2 ⁿ row-column design and where there may be an unknown trend effect that can be expressed as a polynomial function of the position in which observations are obtained in the row-column design. Methods are given for allocating the treatments from a complete or fractional 2-level factorial experiment to rows and columns so that the resulting design yields estimates for main effects that have a high level of resistance against trend effects. Research supported by NSF Grant No. DMS-8700945.     

Forecasting an accumulated series based on partial accumulation II: A new Bayesian method for short series with stable seasonal patterns

The problem of forecasting a time series with only a small amount of data is addressed within a Bayesian framework. The quantity to be predicted is the accumulated value of a positive and continuous variable for which partially accumulated data are available. These conditions appear in a natural way in many situations. A simple model is proposed to describe the relationship between the partial and total values of the variable to be forecasted assuming stable seasonality, which is specified in stochastic terms. Analytical results are obtained for both the point forecast and the entire posterior predictive distribution. The proposed technique does not involve approximations. It allows the use of non-informative priors so that implementation may be automatic. The procedure works well when standard methods cannot be applied due to the reduced number of observations. It also improves on previous results published by the authors. Some real examples are included.