Asymptotic Standard Errors of Generalized Partial Credit Model True Score Equating Using Characteristic Curve Methods

In this study, the delta method was applied to estimate the standard errors of the true score equating when using the characteristic curve methods with the generalized partial credit model in test equating under the context of the common-item nonequivalent groups equating design. Simulation studies were further conducted to compare the performance of the delta method with that of the bootstrap method and the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion empirical standard errors as well as those yielded by the bootstrap method and the multiple imputation method under all the manipulated conditions.

Asymptotic Standard Errors of Parameter Scale Transformation Coefficients in Test Equating Under the Nominal Response Model

Researchers have developed a characteristic curve procedure to estimate the parameter scale transformation coefficients in test equating under the nominal response model. In the study, the delta method was applied to derive the standard error expressions for computing the standard errors for the estimates of the parameter scale transformation coefficients. This brief report presents the results of a simulation study that examined the accuracy of the derived formulas and compared the performance of this analytical method with that of the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion standard errors as well as those yielded by the multiple imputation method under all the simulation conditions.

A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data

MotivationQuantitative mass spectrometry-based proteomics data are characterized by high rates of missing values, which may be of two kinds: missing completely-at-random (MCAR) and missing not-at-random (MNAR). Despite numerous imputation methods available in the literature, none account for this duality, for it would require to diagnose the missingness mechanism behind each missing value.ResultsA multiple imputation strategy is proposed by combining MCAR-devoted and MNAR-devoted imputation algorithms. First, we propose an estimator for the proportion of MCAR values and show it is asymptotically unbiased under assumptions adapted to label-free proteomics data. This allows us to estimate the number of MCAR values in each sample and to take into account the nature of missing values through an original multiple imputation method. We evaluate this approach on simulated data and shows it outperforms traditionally used imputation algorithms.AvailabilityThe proposed methods are implemented in the R package imp4p (available on the CRAN Giai Gianetto (2020)), which is itself accessible through Prostar [email protected]; [email protected]

Regression multiple imputation for missing data analysis

Iterative multiple imputation is a popular technique for missing data analysis. It updates the parameter estimators iteratively using multiple imputation method. This technique is convenient and flexible. However, the parameter estimators do not converge point-wise and are not efficient for finite imputation size m. In this paper, we propose a regression multiple imputation method. It uses the parameter estimators obtained from multiple imputation method to estimate the parameter estimators based on expectation maximization algorithm. We show that the resulting estimators are asymptotically efficient and converge point-wise for small m values, when the iteration k of the iterative multiple imputation goes to infinity. We evaluate the performance of the new proposed methods through simulation studies. A real data analysis is also conducted to illustrate the new method.

Predictive model in the presence of missing data: the centroid criterion for variable selection

IntroductionIn many studies, covariates are not always fully observed because of missing data process. Usually, subjects with missing data are excluded from the analysis but the number of covariates can be greater than the size of the sample when the number of removed subjects is high. Subjective selection or imputation procedures are used but this leads to biased or powerless models.The aim of our study was to develop a method based on the selection of the nearest covariate to the centroid of a homogeneous cluster of covariates. We applied this method to a forensic medicine data set to estimate the age of aborted fetuses.AnalysisMethodsWe measured 46 biometric covariates on 50 aborted fetuses. But the covariates were complete for only 18 fetuses.First, to obtain homogeneous clusters of covariates we used a hierarchical cluster analysis.Second, for each obtained cluster we selected the nearest covariate to the centroid of the cluster, maximizing the sum of correlations (the centroid criterion).Third, with the covariate selected this way, the sample size was sufficient to compute a classical linear regression model.We have shown the almost sure convergence of the centroid criterion and simulations were performed to build its empirical distribution.We compared our method to a subjective deletion method, two simple imputation methods and to the multiple imputation method.ResultsThe hierarchical cluster analysis built 2 clusters of covariates and 6 remaining covariates. After the selection of the nearest covariate to the centroid of each cluster, we computed a stepwise linear regression model. The model was adequate (R2=90.02%) and the cross-validation showed low prediction errors (2.23 10−3).The empirical distribution of the criterion provided empirical mean (31.91) and median (32.07) close to the theoretical value (32.03).The comparisons showed that deletion and simple imputation methods provided models of inferior quality than the multiple imputation method and the centroid method.ConclusionWhen the number of continuous covariates is greater than the sample size because of missing process, the usual procedures are biased. Our selection procedure based on the centroid criterion is a valid alternative to compose a set of predictors.