This paper presents a pseudo-GEE approach to the analysis of longitudinal surveys when the response variable contains missing values. A cycle-specific marginal hotdeck imputation method is proposed to fill in the missing responses and the pseudo-GEE method described in Carrillo et al. (2009) is applied to the imputed data set. Consistency of the resulting pseudo-GEE estimators is established un...

We develop the first statistical matching micro approach reflecting the natural uncertainty arising during the integration of categorical data. A complete synthetic file is obtained by imprecise imputation, replacing missing entries by sets of suitable values. We discuss three imprecise imputation strategies and raise ideas on potential refinements by logical constraints or likelihood-based arg...

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite...

Since the 80's, Statistics Canada has invested in the development of generic software for editing and imputation. From the outset, it has been known by practicians and researchers that treating nonresponse, in particular by imputing for missing values, has impacts on estimates. In the 90's, resources were devoted to research how these impacts could be measured. These studies provided a number o...

Editing of data collected for preparation of statistics is a time and resource consuming process. This paper presents experiments with artificial neural networks as a potential tool for increasing the effectiveness of statistical editing and imputation. To maintain accuracy in resulting statistics, the possibility of deriving reliable accuracy predictions is also discussed.

We propose a Bayesian factor analysis model as an alternative to the Human Development Index (HDI). Our model provides methodology which can either augment or build additional indices. In addition to addressing potential issues of the HDI, we estimate human development with three auxiliary variables capturing environmental health and sustainability, income inequality, and satellite observed nig...

TOPL is a suite of tools to specify operational improvements including ramp metering, incident and demand management, auxiliary lanes, traveler information, and to quickly estimate the benefits that such improvements can realize. TOPL is based on the macroscopic cell transmission model (CTM). Advances under TOPL2 can be classified under (1) theory, (2) software and algorithms, and (3) applicati...

Missing data are vital subject to perform a proper longitudinal analysis. Some just ignore and discard all missing data to have complete dataset. However, it can result in a very substantial loss of information. Therefore, it is important to comprehend imputation methods of handling missing data. This paper discusses four common imputation methods for longitudinal analysis. Then, using simulati...

The paper presents an illustration and empirical study of releasing multiply imputed, fully synthetic public use microdata. Simulations based on data from the US Current Population Survey are used to evaluate the potential validity of inferences based on fully synthetic data for a variety of descriptive and analytic estimands, to assess the degree of protection of confidentiality that is afford...

Nonresponse is common in surveys. When the response probability of a survey variable Y depends on Y through an observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents wi...