On the regression method of estimation of population mean from incomplete survey data through imputation
نویسنده
چکیده
When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation and no imputation are analyzed and the superiority of one method over the other is examined.
منابع مشابه
An Empirical Comparison of Performance of the Unified Approach to Linearization of Variance Estimation after Imputation with Some Other Methods
Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism an...
متن کاملInfluence of Pattern of Missing Data on Performance of Imputation Methods: An Example from National Data on Drug Injection in Prisons
Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...
متن کاملAmputation versus Imputation of Missing Values through Ratio Method in Sample Surveys
In this article we consider the estimation of population mean when some observations on the study characteristic are missing in the bivari ate sample data In all ve estimators are presented and their e ciency properties are discussed One estimator arises from the the amputation of incomplete observations while the remaining four estimators are formu lated using inputed values obtained by the ra...
متن کاملAnalysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values
Estimating the mean and the covariance matrix of an incomplete dataset and filling in missing values with imputed values is generally a nonlinear problem, which must be solved iteratively. The expectation maximization (EM) algorithm for Gaussian data, an iterative method both for the estimation of mean values and covariance matrices from incomplete datasets and for the imputation of missing val...
متن کاملتحلیل درستنمایی ماکزیمم مدل رگرسیون لجستیک در حالتی که داده های متغیرهای پیشگو کامل نیستند ولی متغیرهای کمکی وجود دارند
Background and Objectives: Missing data exist in many studies, e.g. in regression models, and they decrease the model's efficacy. Many methods have been suggested for handling incomplete data: they have generally focused on missing outcome values. But covariate values can also be missing.Materials and Methods: In this paper we study the missing imputation by the EM algorithm and auxiliary varia...
متن کامل