In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean i...

Many large-scale surveys currently use a variety of single imputation methods–as discussed by Chapman (1976), Cox (1980; and Kalton and Kasprzyk (1986)—to handle item nonresponse. Since the use of such imputation increases the underlying variation in the survey results, methods are needed to assess the impact. Until fairly recently, methods to assess the impact of the imputation on the variance...

Encountering non-response is quite prone in sample surveys however smart be the design, which sours the analysis and hence the results. An effort has been made to exploit the non-response by using a completely fresh approach of imputation technique to estimate the population mean in two occasion successive sampling, utilizing completely known auxiliary information which is dynamic in nature and...

THE ANALYSIS OF MISSING DATA IN PUBLIC USE SURVEY DATABASES: A SURVEY OF STATISTICAL METHODS Ping Xu November 20, 2004 Missing data is very common in survey research. However, currently few guidelines exist with regard to the diagnosis and remedy to missing data in survey research. The goal of the thesis was to investigate properties and effects of three selected missing data handling technique...

Missing data is one of the most common challenges facing empirical researchers. This paper presents a general GMM framework for dealing with missing data on explanatory variables or instrumental variables. For a linear-regression model with missing covariate data, an efficient GMM estimator under minimal assumptions on missingness is proposed. The estimator, which also allows for a specificatio...

Nearest neighbor imputation (NNI) is a popular imputation method used to compensate for item nonresponse in sample surveys. Although previous results showed that the NNI sample mean and quantiles are consistent estimators of the population mean and quantiles, large sample inference procedures, such as asymptotic confidence intervals for the population mean and quantiles, are not available. For ...

This paper considers the estimation of coeecients in a linear regression model with missing observations in the independent variables and introduces a modiication of the standard rst order regression method for imputation of missing values. The modiication provides stochastic values for imputation and, as an extension, makes use of the principle of weighted mixed regression. The proposed proced...

In longitudinal clinical trials, missing data are mostly related to dropouts. Some dropouts appear completely at random. The source for other dropouts is withdrawal from trials due to lack of efficacy. For the latter case, the analyses of the actual observed data and completers can produce bias. One of the approaches to comply with the intent-to-treat principle is the imputation of incomplete d...

Item non-response is a challenge faced by virtually all surveys. Item non-response occurs when a respondent skips over a question, refuses to answer a question, or indicates that they do not know the answer to a question. Hot deck imputation is one of the primary item non-response imputation tools used by survey statisticians. Recently, new competitor in the field of Weighted Sequential Hotdeck...

The first study for this dissertation develops a tax-adjusted cost-of-carry model for Australian stock index futures and estimates the value of the debt tax shield, cash dividends and imputation tax credits flowing from the basket stocks in the index. As discussed in the previous chapter, the study relaxes the assumption made in previous research that interest and dividends receive the same tax...