Multiple imputation of incomplete zero‐inflated count data

Empirical count data are often zero‐inflated and overdispersed. Currently, there is no software package that allows adequate imputation of these data. We present multiple‐imputation routines for these kinds of count data based on a Bayesian regression approach or alternatively based on a bootstrap approach that work as add‐ons for the popular multiple imputation by chained equations (mice ) software in R (van Buuren and Groothuis‐Oudshoorn , Journal of Statistical Software, vol. 45, 2011, p. 1). We demonstrate in a Monte Carlo simulation that our procedures are superior to currently available count data procedures. It is emphasized that thorough modeling is essential to obtain plausible imputations and that model mis‐specifications can bias parameter estimates and standard errors quite noticeably. Finally, the strengths and limitations of our procedures are discussed, and fruitful avenues for future theory and software development are outlined.     

Detecting overdispersion in INARCH(1) processes

A statistical test for the degree of overdispersion of count data time series based on the empirical version of the (Poisson) index of dispersion is considered. The test design relies on asymptotic properties of this index of dispersion, which in turn have been analyzed for time series stemming from a compound Poisson (Poisson‐stopped sum) INAR(1) model. This approach is extended to the popular Poisson INARCH(1) model, which exhibits unconditional overdispersion but has an (equidispersed) conditional Poisson distribution. The asymptotic distribution of the index of dispersion if applied to time series stemming from such a model is derived. These results allow us to investigate the ability of the dispersion test to discriminate between Poisson INAR(1) and INARCH(1) models. Furthermore, the question is considered if the index of dispersion could be used to test the null of a Poisson INARCH(1) model against the alternative of an INARCH(1) model with additional conditional overdispersion.     

Person or Job? Change in Person-Job Fit and Its Impact on Employee Work Attitudes over Time

Fit is generally conceptualized as a dynamic construct, but most research on person-environment fit has focused on fit in the current moment. We addressed this oversight by examining the dynamic relationships among person-job (PJ) fit, demand-ability (DA) fit, need-supply (NS) fit, and employee work attitudes over time using a three-wave survey design over a 12-month period. Results from 168 employees revealed that change in PJ fit was significantly related to changes in job satisfaction and affective organizational commitment. In addition, DA and NS fit changes were significantly and indirectly associated with job satisfaction and commitment changes through PJ fit change. We also found that increases in job demands and employee abilities significantly decreased DA fit, and increases in employee needs significantly decreased NS fit whereas increases in job supplies significantly increased NS fit. Finally, we examined age as an important moderator for employees’ reactions to PJ fit changes, and found that younger employees reacted more strongly to increases/decreases in PJ fit than did older employees.     

基于Flexsim的高校餐厅排队仿真

本文以某高校餐厅排队系统为研究对象,首先从运筹学排队论的角度分析排队系统。然后通过实地测量数据进行数据的独立性分析、数据分布形式的假定、分布参数估计以及拟合优度检验来确保仿真输入数据的准确性。最后利用Flexsim软件,对餐厅排队进行仿真,分析输出结果,得到该餐厅应设立的窗口数,从而缓解餐厅的拥挤程度。     

员工心理契约契合度及其评价

作为维系员工与组织关系的心理纽带,员工心理契约的主观期望与实际感知的吻合程度直接影响到员工对于组织的态度与行为。以往文献中对员工心理契约主观期望与实际感知吻合程度的研究缺乏系统性。本文引入心理契约契合度概念,对员工心理契约主观期望与实际感知吻合程度进行测量,提出了构建心理契约契合度评价指标体系的思路及测量方法,最后结合某军工研究所某研发团队的实际情况,对成员心理契约契合度进行了评价。该研究为组织衡量员工心理契约主观期望与实际感知一致性的度量提供了科学依据,增强了心理契约在实际管理工作中的应用性。     

价格离散测度及影响溯源研究——基于在线大数据的经验证据

价格离散是衡量市场整合的重要指标,为全国统一大市场研究提供新的切入点。通过采集同一商品在不同平台商家的日度价格数据,对中国价格离散程度进行测度。结果表明：（1）中国线上市场的价格离散程度总体处于较高水平,且不同类别商品间存在显著异质性。基于此构建的高频价格离散指数(iPDI)发现,指数越低,说明市场统一程度越高,反之统一程度越低。（2）商品价格方差分解发现,价格离散主要源自跨平台竞争,并且构建实证模型,得出跨平台竞争对价格离散呈倒U型关系。（3）将价格离散的微观测度结果应用于宏观层面,基于价格离散特征编制的居民消费价格指数(iCPI_pd),更准确地反映商品价格的波动特征。     

Helmert方差分量估计在测量平差中的算法研究

Helmert(赫尔墨特)方差分量估计作为近代平差随机模型的验后估计,可以准确给出各类观测量之间的权比,提高平差结果的可靠性。文章就该模型在不同观测类型平差数据中的算法进行了探讨,在实际应用中具有重要的参考价值。     

Asymptotically efficient recursive estimation for incomplete data models using the observed information

For a recursive maximum-likelihood estimator with step lengths decaying as 1/n, an adaptive matrix needs to be incorporated to obtain asymptotic efficiency. Ideally, this matrix should be chosen as the inverse Fisher information matrix, which is usually very difficult to compute for incomplete data models. In this paper we give conditions under which the observed information can be incorporated into the recursive procedure to yield an efficient estimator, and we also investigate the finite sample properties of these estimators by simulation.     

Mixture Models of Missing Data

This paper proposes a general framework for the analysis of survey data with missing observations. The approach presented here treats missing data as an unavoidable feature of any survey of the human population and aims at incorporating the unobserved part of the data into the analysis rather than trying to avoid it or make up for it. To handle coverage error and unit non-response, the true distribution is modeled as a mixture of an observable and of an unobservable component. Generally, for the unobserved component, its relative size (the no-observation rate) and its distribution are not known. It is assumed that the goal of the analysis is to assess the fit of a statistical model, and for this purpose the mixture index of fit is used. The mixture index of fit does not postulate that the statistical model of interest is able to account for the entire population rather, that it may only describe a fraction of it. This leads to another mixture representation of the true distribution, with one component from the statistical model of interest and another unrestricted one. Inference with respect to the fit of the model, with missing data taken into account, is obtained by equating these two mixtures and asking, for different no-observation rates, what is the largest fraction of the population where the statistical model may hold. A statistical model is deemed relevant for the population, if it may account for a large enough fraction of the population, assuming the true (if known) or a sufficiently small or a realistic no-observation rate.     

PSEUDO-R2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS

Abstract. A large number of different Pseudo- R ² measures for some common limited dependent variable models are surveyed. Measures include those based solely on the maximized likelihoods with and without the restriction that slope coefficients are zero, those which require further calculations based on parameter estimates of the coefficients and variances and those that are based solely on whether the qualitative predictions of the model are correct or not. The theme of the survey is that while there is no obvious criterion for choosing which Pseudo- R ² to use, if the estimation is in the context of an underlying latent dependent variable model, a case can be made for basing the choice on the strength of the numerical relationship to the OLS- R ² in the latent dependent variable. As such an OLS- R ² can be known in a Monte Carlo simulation, we summarize Monte Carlo results for some important latent dependent variable models (binary probit, ordinal probit and Tobit) and find that a Pseudo- R ² measure due to McKelvey and Zavoina scores consistently well under our criterion. We also very briefly discuss Pseudo- R ² measures for count data, for duration models and for prediction-realization tables.     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.