A Simple Method to Ensure Plausible Multiple Imputation for Continuous Multivariate Data

Multiple Imputation (MI) is an established approach for handling missing values. We show that MI for continuous data under the multivariate normal assumption is susceptible to generating implausible values. Our proposed remedy, is to: (1) transform the observed data into quantiles of the standard normal distribution; (2) obtain a functional relationship between the observed data and it's corresponding standard normal quantiles; (3) undertake MI using the quantiles produced in step 1; and finally, (4) use the functional relationship to transform the imputations into their original domain. In conclusion, our approach safeguards MI from imputing implausible values.     

Fractional Regression Hot Deck Imputation Weight Adjustment

Fractional regression hot deck imputation (FRHDI) imputes multiple values for each instance of a missing dependent variable. The imputed values are equal to the predicted value plus multiple random residuals. Fractional weights enable variance estimation and preserve correlations. In some circumstances with some starting weight values, existing procedures for computing FRHDI weights can produce negative values. We discuss procedures for constructing non-negative adjusted fractional weights for FRHDI and study performance of the algorithm using simulation. The algorithm can be used effectively with FRDHI procedures for handling missing data in the context of a complex sample survey.     

Analysis of mixed correlated semi-continuous and β-inflated Poisson responses

ABSTRACT

In this article, inflation at an arbitrary point β of a member of power series exponential family and mean-inflation as a cause of having semi-continuous distribution are discussed. Also, a joint modeling of such a semi-continuous response and β-inflated Poisson response is presented. Simultaneous effects of covariates on both responses, which have two-component mixture distributions, are investigated. To find the parameter estimates, the maximum likelihood approach is used. The proposed model is illustrated on some simulation studies and applied to a real survey dataset.     

Comparisons of imputation methods with application to assess factors associated with self efficacy of physical activity in breast cancer survivors

ABSTRACT

Missing data are commonly encountered in self-reported measurements and questionnaires. It is crucial to treat missing values using appropriate method to avoid bias and reduction of power. Various types of imputation methods exist, but it is not always clear which method is preferred for imputation of data with non-normal variables. In this paper, we compared four imputation methods: mean imputation, quantile imputation, multiple imputation, and quantile regression multiple imputation (QRMI), using both simulated and real data investigating factors affecting self-efficacy in breast cancer survivors. The results displayed an advantage of using multiple imputation, especially QRMI when data are not normal.     

Methods for Handling Missing Secondary Respondent Data

Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. The authors reviewed, evaluated, and tested solutions to this problem. Five strategies of dealing with missing partner data were reviewed: (a) complete case analysis, (b) inverse probability weighting, (c) correction with a Heckman selection model, (d) maximum likelihood estimation, and (e) multiple imputation. Two approaches were used to evaluate the performance of these methods. First, the authors used data from the National Survey of Fertility Barriers (n = 1,666) to estimate a model predicting marital quality based on characteristics of women and their husbands. Second, they conducted a simulation testing the 5 methods and compared the results to estimates where the true value was known. They found that the maximum likelihood and multiple imputation methods were advantageous because they allow researchers to utilize all of the available information as well as produce less biased and more efficient estimates.     

违约归责原则的历史演进

在考察古罗马法以及近现代民法的基础上 ,阐明了违约归责原则的历史演进过程 ,即从结果归责到过错归责再到严格责任原则的发展过程。同时 ,还分析了我国合同法上的违约归责原则。     

MISSING VALUES, IMPUTATION AND ERROR RATE ESTIMATORS IN DISCRIMINANT ANALYSIS

Error rate is a popular criterion for assessing the performance of an allocation rule in discriminant analysis. Training samples which involve missing values cause problems for those error rate estimators that require all variables to be observed at all data points. This paper explores imputation algorithms, their effects on, and problems of implementing them with, eight commonly used error rate estimators (three parametric and five non-parametric) in linear discriminant analysis. The results indicate that imputation should not be based on the way error rate estimators are calculated, and that imputed values may underestimate error rates.     

Addressing the problem of missing data in decision tree modeling

Tree-based models (TBMs) can substitute missing data using the surrogate approach (SUR). The aim of this study is to compare the performance of statistical imputation against the performance of SUR in TBMs. Employing empirical data, a TBM was constructed. Thereafter, 10%, 20%, and 40% of variable values appeared as the first split was deleted, and imputed with and without the use of outcome variables in the imputation model (IMP? and IMP+). This was repeated one thousand times. Absolute relative bias above 0.10 was defined as sever (SARB). Subsequently, in a series of simulations, the following parameters were changed: the degree of correlation among variables, the number of variables truly associated with the outcome, and the missing rate. At a 10% missing rate, the proportion of times SARB was observed in either SUR or IMP? was two times higher than in IMP+ (28% versus 13%). When the missing rate was increased to 20%, all these proportions were approximately doubled. Irrespective of the missing rate, IMP+ was about 65% less likely to produce SARB than SUR. Results of IMP? and SUR were comparable up to a 20% missing rate. At a high missing rate, IMP? was 76% more likely to provide SARB estimates. Statistical imputation of missing data and the use of outcome variable in the imputation model is recommended, even in the content of TBM.     

对"加害人不明情形"类案件责任承担适用原则的探析

在加害人不明情形下由全部"可能加害人"共同承担责任的判决缺乏事实根据和法学理论、法律制度的支持,同时又会产生许多不良的社会效果,甚至会诱发当事人的道德风险.确定加害人不明情形下的损害承担原则,应当转换思路,改由受害人自担风险.     

完善和发展国家赔偿制度——基于公民权益救济的立场、赔偿与补偿协调的角度

完善国家赔偿制度可以对现行国家赔偿法进行修补,更好的选择是制定一部新的国家赔偿法.制定新的国家赔偿法,应当协调国家赔偿和国家补偿制度,强调通过国家赔偿救济公民权益的意义,这就需要:对国家赔偿的范围作慎重考虑,适当扩大国家赔偿范围;对国家赔偿归责原则作全面检讨,确立违法归责为原则、结果归责为例外的归责体系;认真对待由国家侵权产生的精神损害问题,确立国家侵权精神赔偿制度;着重于完善国家赔偿的相关程序和机制,维护赔偿程序的正当性和保障机制的有效性.