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Hittade 4 uppsatser som matchar ovanstående sökkriterier.

1. 1. Missing Data - A Gentle Introduction

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Vilgot Österlund; [2020]
   Nyckelord :Missing data; Small samples; Multiple imputation; Maximum likelihood; Listwise deletion; Missing at random; Missing completely at random; Linear regression; Logistic regression.;

   Sammanfattning : This thesis provides an introduction to methods for handling missing data. A thorough review of earlier methods and the development of the field of missing data is provided. The thesis present the methods suggested in today’s literature, multiple imputation and maximum likelihood estimation. LÄS MER

3. 2. Estimation of Regression Coefficients under a Truncated Covariate with Missing Values

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Ragna Reinhammar; [2019]
   Nyckelord :Key words: missing data handling; linear regression; truncated normal distribution; EM-algorithm; Listwise Deletion; MICE;

   Sammanfattning : By means of a Monte Carlo study, this paper investigates the relative performance of Listwise Deletion, the EM-algorithm and the default algorithm in the MICE-package for R (PMM) in estimating regression coefficients under a left truncated covariate with missing values. The intention is to investigate whether the three frequently used missing data techniques are robust against left truncation when missing values are MCAR or MAR. LÄS MER

4. 3. Effects of Missing Values on Neural Network Survival Time Prediction

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Torrin Raoufi-Danner; [2018]
   Nyckelord :Machine Learning; Neural Network; Imputation; Breast Cancer;

   Sammanfattning : Data sets with missing values are a pervasive problem within medical research. Building lifetime prediction models based solely upon complete-case data can bias the results, so imputation is preferred over listwise deletion. LÄS MER

5. 4. Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism

   Master-uppsats, Statistiska institutionen

   Författare :Wensi Pan; [2012]
   Nyckelord :Missing not at Random; Listwise Deletion; Multiple Imputation;

   Sammanfattning : In this article, we propose an overview of missing data problem, introduce three missing data mechanisms and study general solutions to them when estimating a linear regression equation. When we have partly missing data, there are two common ways to solve this problem. One way is to ignore those records with missing values. LÄS MER