Missing or incomplete responses are a common feature with many censuses and sample surveys. The problem created by survey non-response is that data values intended by the survey design to be observed are in fact missing. These missing values not only mean less efficient estimates because of the reduced size of the database, but also that standard complete-data methods cannot be used to analyse ...

Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two f...

One of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data. Many statistical procedures cannot be used immediately if any values are missing. Imputation of missing data before starting statistical analysis is then necessary. This paper proposes imputation methods of the mean based on indirect estimators of available cases. A complete ...

This report provides a review of the multiple imputation methodology used by the Sponsor (Acorn Cardiovascular, Inc.) to handle missing data in the analysis of the primary endpoint of its pivotal clinical trial of the CorCap Cardiac Support Device (CorCap), as well as an evaluation of the impact of multiple imputation on the results of that analysis. This included a completely blinded imputatio...

Missing data are often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. This article reviews several imputation methods used in the social sciences and discusses advantages and disadvantages of these methods in practice. Simpler imputation methods as well as more advanced methods, such as fractional and mu...

Fractional hot deck imputation, considered in Fuller and Kim (2005), is extended to multivariate missing data. The joint distribution of the study items is nonparametrically estimated using a discrete approximation, where the discrete transformation also serves to define imputation cells. The procedure first estimates the probabilities for the cells and then imputes real observations for missin...

Variance estimation for estimators of state, county, and school district quantities derived from the Census 2000 long form are discussed. The variance estimator must account for (1) uncertainty due to imputation, and (2) raking to census population controls. An imputation procedure that imputes more than one value for each missing item using donors that are neighbors is described and the proced...

MI is usually performed under the assumption that the mechanism causing the missing data is ‘Missing At Random’. We discuss the practical implications of this elsewhere (Carpenter and Kenward, 2008). Here we note that although this assumption may be plausible, it cannot be verified from the data at hand, and therefore the analysis of a partially observed data set under the MAR assumption can ne...

A dividend imputation tax system provides shareholders with a tax credit that can be used to offset personal tax on dividend income. The size of this credit depends on tax paid at the corporate level so that the “double taxation” of dividends is effectively eliminated. This paper shows how to infer the value of imputation tax credits (which is an important input into the weighted-average cost o...

We deal with the effect of missing data under a ”Missing at Random Model” on classification of variables with non hierarchical methods. The partitions are compared by the Rand’s index.