Search Results - subject:"missing data"

Analysis of partially observed clustered data using generalized estimating equations and multiple imputation

Aloisio, Kathryn M.; Swanson, Sonja A.; Micali, Nadia; … - In: Stata Journal 14 (2014) 4, pp. 863-883

generalized estimating equation (GEE) framework. However, these studies often have missing data because additional stages of … and Rubin (2002, Statistical Analysis with Missing Data [Wiley]). This is a strong assumption that may not be tenable …

Persistent link: https://www.econbiz.de/10011105649

Transmission of preferences and beliefs about female labor market participation: direct evidence on the role of mothers

Carro, Jesus; Machado, Matilde Pinto; Mora, Ricardo - C.E.P.R. Discussion Papers - 2014

Recently, economists have established that culture defined as a common set of preferences and beliefs—affects economic outcomes, including the levels of female labor force participation. Although this literature has argued that culture is transmitted from parents to children, it has also...

Persistent link: https://www.econbiz.de/10011083298

ANALYSIS OF MARKET VOLATILITY VIA A DYNAMICALLY PURIFIED OPTION PRICE PROCESS

LUONG, CHUONG; DOKUCHAEV, NIKOLAI - In: Annals of Financial Economics (AFE) 09 (2014) 03, pp. 1450006-1

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial "dynamically purified" price process that in theory allows to eliminate the impact of the stock price movements....

Persistent link: https://www.econbiz.de/10011200059

Mean field variational Bayesian inference for support vector machine classification

Luts, Jan; Ormerod, John T. - In: Computational Statistics & Data Analysis 73 (2014) C, pp. 163-176

classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and …

Persistent link: https://www.econbiz.de/10010738195

Model selection and model averaging after multiple imputation

Schomaker, Michael; Heumann, Christian - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 758-770

interpretation and confidence interval construction. These problems become even more complex in the presence of missing data and it … averaging in the context of missing data is proposed. The focus lies on multiple imputation as a strategy to deal with the …

Persistent link: https://www.econbiz.de/10010871378

Variable selection by Random Forests using data with missing values

Hapfelmeier, A.; Ulm, K. - In: Computational Statistics & Data Analysis 80 (2014) C, pp. 129-139

Variable selection has been suggested for Random Forests to improve data prediction and interpretation. However, the basic element, i.e. variable importance measures, cannot be computed straightforward when there are missing values in the predictor variables. Possible solutions are multiple...

Persistent link: https://www.econbiz.de/10010906927

Combining Forecasts with Missing Data: Making Use of Portfolio Theory

Fastrich, Björn; Winker, Peter - In: Computational Economics 44 (2014) 2, pp. 127-152

weights-shifting scheme that prevents the exclusion of analysts with missing data and the resulting loss of information. The …

Persistent link: https://www.econbiz.de/10010989265

Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis

Ginkel, Joost; Kroonenberg, Pieter - In: Journal of Classification 31 (2014) 2, pp. 242-269

Multiple imputation is one of the most highly recommended procedures for dealing with missing data. However, to date … can be used to represent the uncertainty due to the missing data. In two simulation studies, the performance of … methods either may run into serious problems or are not able to adequately assess the accuracy due to the presence of missing …

Persistent link: https://www.econbiz.de/10010950404

Second-order asymptotic theory for calibration estimators in sampling and missing-data problems

Tan, Zhiqiang - In: Journal of Multivariate Analysis 131 (2014) C, pp. 240-253

estimators are second-order unbiased but the regression estimators are not. For the missing-data problem and survey sampling, no …

Persistent link: https://www.econbiz.de/10010930746

A 0-1 quadratic programme for the case of missing data in regression

Smith, Brian K.; Chimka, Justin R.; Nachtmann, Heather - In: International Journal of Data Analysis Techniques and … 6 (2014) 1, pp. 94-104

Multivariate statistical analysis techniques including regression analysis compose a popular toolset for analysing survey data, but the techniques require a complete dataset with no missing values. Unfortunately, most survey datasets contain missing values. These missing values must be resolved...

Persistent link: https://www.econbiz.de/10010741433