制度缺失与储蓄替代——中国农户防卫性储蓄机制的行为发现与理论检验

运用统计图解方法研究，发现中国农户家庭偏好中存在防卫性机制。适当选取模型和计量工具，随后的参数检验也证实防卫性机制存在。防卫性储蓄是金融保险制度缺失条件下的行为替代，决策应沿着合理利用这一行为机制来思考，以建立城乡一体的国家社保体系。     

Treatment and reporting of item-level missing data in social science research

Most quantitative studies in the social sciences suffer from missing data. However, despite the large availability of documents and software to treat such data, it appears that many social scientists do not apply good practices regarding missing data. We analyzed quantitative papers published in 2017 in six top-level social science journals. Item-level missing data was found in at least 69.5% of the papers, but their presence was explicitly reported in only 44.4% of all analyzed papers. Moreover, in the majority of cases, the treatments applied to missing data were incorrect, with many uses of deletion methods that are known to produce biased results and to reduce statistical power. The impact of missing data and of their treatment on results was barely discussed. Results show that social scientists underestimate the impact of missing data on their research and that they should pay more attention to the way such data are treated.     

A doubly robust goodness-of-fit test in general linear models with missing covariates

In this article, we utilize a form of general linear model where missing data occurred randomly on the covariates. We propose a test function based on the doubly robust method to investigate goodness of fit of the model. For this aim, kernel method is used to estimate unknown functions under estimating equation method. Doubly robustness and asymptotic properties of the test function are obtained under local and alternative hypotheses. Furthermore, we investigate the power of the proposed test function by means of some simulation studies and finally we apply this method on analyzing a real dataset.     

Efficient estimation in a regression model with missing responses

This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected by the estimator of the regression parameter, and by the imputation method. To estimate the regression parameter, the ordinary least squares is efficient only if the error distribution happens to be normal. If the errors are not normal, then we propose a one step improvement estimator or a maximum empirical likelihood estimator to efficiently estimate the parameter.To investigate the imputation’s impact on the estimation of the mean response, we compare the listwise deletion method and the propensity score method (which do not use imputation at all), and two imputation methods. We demonstrate that listwise deletion and the propensity score method are inefficient. Partial imputation, where only the missing responses are imputed, is compared to full imputation, where both missing and non-missing responses are imputed. Our results reveal that, in general, full imputation is better than partial imputation. However, when the regression parameter is estimated very poorly, the partial imputation will outperform full imputation. The efficient estimator for the mean response is the full imputation estimator that utilizes an efficient estimator of the parameter.     

Equivalence of two tests in varying coefficient partially linear errors in variable model with missing responses

This paper considers two tests on varying coefficient partially linear errors-in-variables models (VCPLM-EV) with missing responses under the linear constraint. The restricted estimator for the parametric component is derived and proven to share asymptotically normal distribution. In order to test the linear constraint, two statistics based on the profile Lagrange multiplier method and the corrected residual sum of squares method respectively, are proposed. It is of interest to obtain that the magnitudes of the two statistics are equal exactly and follow the asymptotical chi-square distribution. This reveals a new type of Wilk’s phenomenon in VCPLM-EV models with missing response. Finally, some numerical examples are carried out to illustrate relevant performances.     

Do required fields in online surveys in the social sciences impair reliability?

ABSTRACT

The present study is the first to examine empirically whether required fields in online surveys impair reliability and response pattern, as participants forced to respond to all items may provide arbitrary answers. Two hundred and thirteen participants completed a survey consisting of six questionnaires testing personal and social issues and perceptions. They were randomly assigned to one of two versions of the survey: optional-fields (N = 104) or required-fields (N = 109). Comparison of the Cronbach’s alpha of the two versions revealed identical reliability values for all questionnaires, save for somatization, where a minor difference was found. Confirmatory factor analysis showed no difference in the factor structure of the two versions, and no differences were found by Bayesian t-test and Levene’s test for equality of variances. The findings suggest that required fields do not impair reliability or change the response pattern, and therefore can be used in online surveys to prevent missing data.     

Refusal to Answer Specific Questions in a Survey: A Case Study

It is well known that non ignorable item non response may occur when the cause of the non response is the value of the latent variable of interest. In these cases, a refusal by a respondent to answer specific questions in a survey should be treated sometimes as a non ignorable item non response. The Rasch-Rasch model (RRM) is a new two-dimensional item response theory model for addressing non ignorable non response. This article demonstrates the use of the RRM on data from an Italian survey focused on assessment of healthcare workers’ knowledge about sudden infant death syndrome (that is, a context in which non response is presumed to be more likely among individuals with a low level of competence). We compare the performance of the RRM with other models within the Rasch model family that assume the unidimensionality of the latent trait. We conclude that this assumption should be considered unreliable for the data at hand, whereas the RRM provides a better fit of the data.     

Interpolation,outliers mid inverse autocorrelations

The paper addresses the problem of estimating missing observations in an infinite realization of a linear, possibly nonstationary, stochastic processes when the model is known. The general case of any possible distribution of missing observations in the time series is considered, and analytical expressions for the optimal estimators and their associated mean squared errors are obtained. These expressions involve solely the elements of the inverse or dual autocorrelation function of the series.

This optimal estimator -the conditional expectation of the missing observations given the available ones- is equal to the estimator that results from filling the missing values in the series with arbitrary numbers, treating these numbers as additive outliers, and removing with intervention analysis the outlier effects from the invented numbers.     

Multivariate regression models for discrete and continuous repeated measurements

A general class of multivariate regression models is considered for repeated measurements with discrete and continuous outcome variables. The proposed model is based on the seemingly unrelated regression model (Zellner, 1962) and an extension of the model of Park and Woolson(1992). The regression parameters of the model are consistently estimated using the two-stage least squares method. When the out come variables are multivariate normal, the two-stage estimator reduces to Zellner’s two-stage estimator. As a special case, we consider the marginal distribution described by Liang and Zeger (1986). Under this this distributional assumption, we show that the two-stage estimator has similar asymptotic properties and comparable small sample properties to Liang and Zeger's estimator. Since the proposed approach is based on the least squares method, however, any distributional assumption is not required for variables outcome variables. As a result, the proposed estimator is more robust to the marginal distribution of outcomes.     

Nonparametric estimation for survival data with censoring indicators missing at random

In this paper, we consider the problem of hazard rate estimation in the presence of covariates, for survival data with censoring indicators missing at random. We propose in the context usually denoted by MAR (missing at random, in opposition to MCAR, missing completely at random, which requires an additional independence assumption), nonparametric adaptive strategies based on model selection methods for estimators admitting finite dimensional developments in functional orthonormal bases. Theoretical risk bounds are provided, they prove that the estimators behave well in term of mean square integrated error (MISE). Simulation experiments illustrate the statistical procedure.