We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record. In particular, we will be concerned with data sets involving a mixture of numeric, ordinal, binary, and categorical variables. Our technique is a variation of the popular nearest neighbor hot deck imputation NNHDI where “ne...

Electronic health records (EHRs) have become a vital source of patient outcome data but the widespread prevalence of missing data presents a major challenge. Different causes of missing data in the EHR data may introduce unintentional bias. Here, we compare the effectiveness of popular multiple imputation strategies with a deeply learned autoencoder using the Pooled Resource Open-Access ALS Cli...

The data produced and consumed during emergency and disaster management is inherently spatial in nature, with a high probability of information deluge. Disaster management process are time and safety-critical and requires processes and data standards which are inter-operable. The Open Geo-spatial Consortium provides specifications for geo-spatial data, of which WFS, WMS, GML, GeoXaCML and GeoSM...

February 19, 2015 Type Package Title Statistical Matching Version 1.2.3 Date 2015-01-28 Author Marcello D'Orazio Maintainer Marcello D'Orazio Depends R (>= 2.7.0), proxy, clue, survey, RANN, lpSolve Suggests MASS, Hmisc Description Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions...

In this paper, we propose two tests for parametric models belonging to the Archimedean copula family, one for uncensored bivariate data and the other one for right-censored bivariate data. Our test procedures are based on the Fisher transform of the correlation coefficient of a bivariate (U, V ), which is a one-toone transform of the original random pair (T1, T2) that can be modeled by an Archi...

This paper considers the estimation of coe cients in a linear regression model with missing obser vations in the independent variables and introduces a modi cation of the standard rst order regression method for imputation of missing values The modi cation provides stochastic values for imputation and as an extension makes use of the principle of weighted mixed regression The proposed proce dur...

JONES, SHELTON MAURICE. Improved Variance Estimators Using Weighting Class Adjustments for Sample Survey Nonresponse. (Under the direction of Robert G. D. Steel.) In large-scale sample surveys, there will always be the problem of nonresponse. There are several imputation procedures designed to adjust for nonresponding sampling units. Among these are weighting class adjustments. One of the metho...

This paper considers partially linear varying coefficient models when the response variable is missing at random. The paper uses imputation techniques to develop an omnibus specification test. The test is based on a simple modification of a Cramer von Mises functional that overcomes the curse of dimensionality often associated with the standard Cramer von Mises functional. The paper also consid...

The paper considers the problem of testing the hypothesis that J ≥ 2 dependent groups have equal population measures of location when using a robust estimator and there are missing values. For J = 2, methods have been studied based on trimmed means. But the methods are not readily extended to the case J > 2. Here, two alternative test statistics were considered, one of which performed poorly in...

Missing values and dropouts are common issues in longitudinal studies in all areas of medicine and public health. Intent-to-treat (ITT) analysis has become a widely accepted method for the analysis of controlled clinical trials. In most controlled clinical trials, some patients do not complete their intended followup according to the protocol for a variety of reasons; this problem generates mis...