Andere Kunden interessierten sich auch für
![Logistic Regression with Missing Values in the Covariates]( "Logistic Regression with Missing Values in the Covariates")
Logistic Regression with Missing Values in the Covariates42,99 €![Robust Random Regression Imputation for Missing Data]( "Robust Random Regression Imputation for Missing Data")
Robust Random Regression Imputation for Missing Data46,99 €![Estimation of Hedonic Regression Models with Missing Observations]( "Estimation of Hedonic Regression Models with Missing Observations")
Estimation of Hedonic Regression Models with Missing Observations40,99 €![On Some Types of Bitopological Groups with respect to ij-¿-Open Sets]( "On Some Types of Bitopological Groups with respect to ij-¿-Open Sets")
On Some Types of Bitopological Groups with respect to ij-¿-Open Sets26,99 €![Approximation of Eigen Values of Ordinary Differential Operators]( "Approximation of Eigen Values of Ordinary Differential Operators")
Approximation of Eigen Values of Ordinary Differential Operators26,99 €![The Dynamics of Prey-Predator Model with Different Types of Functional responses]( "The Dynamics of Prey-Predator Model with Different Types of Functional responses")
The Dynamics of Prey-Predator Model with Different Types of Functional responses36,99 €![Image Encryption with Singular Values Decomposition Aided]( "Image Encryption with Singular Values Decomposition Aided")
Image Encryption with Singular Values Decomposition Aided36,99 €
Missing data, an issue frequently encountered in data analysis, causes difficulties with estimation, precision and inference. Methods for dealing with missing data issues have been studied extensively in the last few decades. All disciplines that require data collection have encountered missing data at one time or another. For various reasons, two types of missing values can be present in the same dataset. New questions about the probabilistic mechanisms generating the two types of missing values, and the conditions under which these mechanisms can be partially or completely ignored, are discussed in length. Two-stage multiple imputation (MI), an extension of conventional MI, is introduced together with two real-data examples which demonstrate the implementation of two-stage MI. In the first example the missing data mechanisms are completely ignored. In the second example the missing data mechanisms are partially ignored and partially modeled. The material covered in this book will be especially useful for professionals in the Statistics and Biostatistics fields, or anyone else who works with incomplete data.