Effects of Rotation Group Bias on Estimation of Unemployment

Previous analysis of rotation group bias in the Current Population Survey has concluded that if the biases are additive, the ratio and composite estimators of month-to-month change in unemployment are unbiased. This article shows that if the biases contain a multiplicative aspect, both estimators of change are then biased. The article also presents some empirical results that cast doubt on the validity of a purely additive model.     

How Long Is a Spell of Unemployment? Illusions and Biases in the Use of CPS Data

Most data used to study the durations of unemployment spells come from the Current Population Survey (CPS), which is a point-in-time survey and gives an incomplete picture of the underlying duration distribution. We introduce a new sample of completed unemployment spells obtained from panel data and apply CPS sampling and reporting techniques to replicate the type of data used by other researchers. Predicted duration distributions derived from this CPS-like data are then compared to the actual distribution. We conclude that the best inferences that can be made about unemployment durations by using CPS-like data are seriously biased.     

Estimating Gross Labor-Force Flows

We present and apply an adjustment procedure for the Bureau of the Census and Bureau of Labor Statistics gross labor-force flows data that addresses two major defects in the data. First, an adjustment procedure is developed to take account of individuals with missing labor-force classifications who are not missing at random. Second, we provide a procedure for adjustment for individuals with spurious labor-force transitions arising because of classification errors in either the current or the previous Current Population Survey. Our procedures are applied to compute adjusted monthly gross change data for the period January 1977–December 1982. The average adjustment for nonrandom missing classifications ranges from –12% to 15% of the unadjusted gross change data. The average adjustment for spurious labor-force transitions reduces estimated movements by 8%–49%. The classification adjustment also increases estimated consecutive periods of unemployment by 18%. We apply several internal and external consistency checks to our procedure. In general, the adjustments appear reasonable. We also suggest some modifications of Current Population Survey procedures that could reduce the use of ex post adjustment procedures in the future.     

Keeping Time: An Analysis of Errors in the Measurement of Unemployment Duration

This study uses longitudinal microdata from the Current Population Survey to examine the consistency with which unemployment duration is reported. An estimate of measurement error is proposed and analyzed. A significant negative relationship is found between the initial month's unemployment duration and the reported change between surveys. On average, persons who begin spells of unemployment in the second month report durations greater than the actual time elapsed. Overall, the average duration of unemployment in this sample may be overstated by at least two weeks. A brief discussion of some implications for empirical labor market research concludes the article.     

Estimation in Varying-Coefficient Errors-in-Variables Models with Missing Response Variables

This article is concerned with the estimation of a varying-coefficient regression model when the response variable is sometimes missing and some of the covariates are measured with additive errors. We propose a class of estimators for the coefficient functions, as well as for the population mean and the error variance. The resulting estimators are shown to be asymptotically normal. Simulation studies are conducted to illustrate our approach.     

Multi-state Models in Epidemiology

I first discuss the main assumptions which can be made for multi-state models: the time-homogeneity and semi-Markov assumptions, the problem of choice of the time scale, the assumption of homogeneity of the population and also assumptions about the way the observations are incomplete, leading to truncation and censoring. The influence of covariates and different durations and time-dependent variables are synthesized using explanatory processes, and a general additive model for transition intensities presented. Different inference approaches, including penalized likelihood, are considered. Finally three examples of application in epidemiology are presented and some references to other works are given.     

Nonparametric Modeling Auxiliary Covariates in Random Coefficient Models

Random coefficient model (RCM) is a powerful statistical tool in analyzing correlated data collected from studies with different clusters or from longitudinal studies. In practice, there is a need for statistical methods that allow biomedical researchers to adjust for the measured and unmeasured covariates that might affect the regression model. This article studies two nonparametric methods dealing with auxiliary covariate data in linear random coefficient models. We demonstrate how to estimate the coefficients of the models and how to predict the random effects when the covariates are missing or mismeasured. We employ empirical estimator and kernel smoother to handle a discrete and continuous auxiliary, respectively. Simulation results show that the proposed methods perform better than an alternative method that only uses data in the validation data set and ignores the random effects in the random coefficient model.     

A note on iterative AK composite estimator for Current Population Survey

In this article, we introduce the iterative AK composite estimator for the Current Population Survey. This estimator adopts the AK composite estimator as the initial value and further makes good use of the intrinsic composite scheme of the AK composite estimator. We derive the mean squared error (MSE) formula for the iterative composite estimator and describe how to select the optimal tuning coefficients by minimising the MSE. Finally, we examine the proposed method through a simulation study.     

Models with Errors due to Misreported Measurements

Measurement error and misclassification models feature prominently in the literature. This paper describes misreporting error, which can be considered to fall somewhere between these two broad types of model. Misreporting is concerned with situations where a continuous random variable X is measured with error and only reported as the discrete random variable Z. Data grouping or rounding are the simplest examples of this, but more generally X may be reported as a value z of Z which refers to a different interval from the one in which X lies. The paper discusses a method for handling misreported data and draws links with measurement error and misclassification models. A motivating example is considered from a prenatal Down's syndrome screening, where the gestational age at which mothers present for screening is a true continuous variable but is misreported because it is only ever observed as a discrete whole number of weeks which may in fact be in error. The implications this misreporting might have for the screening are investigated.     

Likelihood-Based Inference for Multivariate Skew-Normal Regression Models

In this article, we present EM algorithms for performing maximum likelihood estimation for three multivariate skew-normal regression models of considerable practical interest. We also consider the restricted estimation of the parameters of certain important special cases of two models. The methodology developed is applied in the analysis of longitudinal data on dental plaque and cholesterol levels.     

Approximate bounded influence estimation for longitudinal data with outliers and measurement errors

Mixed effects models or random effects models are popular for the analysis of longitudinal data. In practice, longitudinal data are often complex since there may be outliers in both the response and the covariates and there may be measurement errors. The likelihood method is a common approach for these problems but it can be computationally very intensive and sometimes may even be computationally infeasible. In this article, we consider approximate robust methods for nonlinear mixed effects models to simultaneously address outliers and measurement errors. The approximate methods are computationally very efficient. We show the consistency and asymptotic normality of the approximate estimates. The methods can also be extended to missing data problems. An example is used to illustrate the methods and a simulation is conducted to evaluate the methods.     

Multi-state Models: A Review

Multi-state models are models for a process, for example describing a life history of an individual, which at any time occupies one of a few possible states. This can describe several possible events for a single individual, or the dependence between several individuals. The events are the transitions between the states. This class of models allows for an extremely flexible approach that can model almost any kind of longitudinal failure time data. This is particularly relevant for modeling different events, which have an event-related dependence, like occurrence of disease changing the risk of death. It can also model paired data. It is useful for recurrent events, but has limitations. The Markov models stand out as much simpler than other models from a probability point of view, and this simplifies the likelihood evaluation. However, in many cases, the Markov models do not fit satisfactorily, and happily, it is reasonably simple to study non-Markov models, in particular the Markov extension models. This also makes it possible to consider, whether the dependence is of short-term or long-term nature. Applications include the effect of heart transplantation on the mortality and the mortality among Danish twins.     

经济、管理类专业《统计学》课程建设的几个基本问题

内容提要：本人认为,目前在我国经济、管理类专业《统计学》课程建设中尚有一些基本理论问题需要讨论清楚。例如,怎样理解“统计学是一门数据科学”这一命题,以及课程改革后的《统计学》与改革前的《社会经济统计学原理》究竟有什么本质上的区别;《统计学》课程中用于对重复随机试验进行统计推断的样本和抽样调查中的样本有什么区别和联系;应用社会经济调查数据进行统计分析较之应用实验数据进行统计分析有什么特殊性的问题需要解决,等等。文章找出了这些问题的若干误区并阐述了作者的个人见解。     

Bias-Corrected Estimation in Dynamic Panel Data Models

This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model and derives its limiting distribution for finite number of time periods, T, and large number of cross-section units, N. The bias-corrected estimator is derived as a bias correction of the least squares dummy variable (within) estimator. It does not share some of the drawbacks of recently developed instrumental variables and generalized method-of-moments estimators and is relatively easy to compute. Monte Carlo experiments provide evidence that the bias-corrected estimator performs well even in small samples. The proposed technique is applied in an empirical analysis of unemployment dynamics at the U.S. state level for the 1991–2000 period.     

A Baseline-free Procedure for Transformation Models Under Interval Censorship

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.     

An Estimate of a Change Point in Variance of Measurement Errors and Its Convergence Rate

In this article, an estimate of a change point in variance of measurement errors (ME) is given in terms of characteristic functions when the variances are known. Its modification is also given for the case that the variances are unknown. In addition, the consistency and convergence rates of the estimator and its modification are investigated. The simulation study shows that the proposed estimators perform well.     

Statistical Inference for Partially Hidden Markov Models

ABSTRACT

In this article we introduce a new missing data model, based on a standard parametric Hidden Markov Model (HMM), for which information on the latent Markov chain is given since this one reaches a fixed state (and until it leaves this state). We study, under mild conditions, the consistency and asymptotic normality of the maximum likelihood estimator. We point out also that the underlying Markov chain does not need to be ergodic, and that identifiability of the model is not tractable in a simple way (unlike standard HMMs), but can be studied using various technical arguments.     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

The Method to Identify a Biomarker and to Evaluate Its Efficiency for Survival by Using the Joint Model of the Accelerate Failure Time and Longitudinal Data

In this article, we discuss how to identify longitudinal biomarkers in survival analysis under the accelerated failure time model and also discuss the effectiveness of biomarkers under the accelerated failure time model. Two methods proposed by Shcemper et al. are deployed to measure the efficacy of biomarkers. We use simulations to explore how the factors can influence the power of a score test to detect the association of a longitudinal biomarker and the survival time. These factors include the functional form of the random effects from the longitudinal biomarkers, in the different number of individuals, and time points per individual. The simulations are used to explore how the number of individuals, the number of time points per individual influence the effectiveness of the biomarker to predict survival at the given endpoint under the accelerated failure time model. We illustrate our methods using a prothrombin index as a predictor of survival in liver cirrhosis patients.     

A heteroscedastic structural errors-in-variables model with equation error

It is not uncommon with astrophysical and epidemiological data sets that the variances of the observations are accessible from an analytical treatment of the data collection process. Moreover, in a regression model, heteroscedastic measurement errors and equation errors are common situations when modelling such data. This article deals with the limiting distribution of the maximum-likelihood and method-of-moments estimators for the line parameters of the regression model. We use the delta method to achieve it, making it possible to build joint confidence regions and hypothesis testing. This technique produces closed expressions for the asymptotic covariance matrix of those estimators. In the moment approach we do not assign any distribution for the unobservable covariate while with the maximum-likelihood approach, we assume a normal distribution. We also conduct simulation studies of rejection rates for Wald-type statistics in order to verify the test size and power. Practical applications are reported for a data set produced by the Chandra observatory and also from the WHO MONICA Project on cardiovascular disease.