Results 161 to 170 of about 762,527 (191)

A test of missing completely at random for longitudinal data with missing observations

Statistics in Medicine, 1997
Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results.
T, Park, S Y, Lee
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A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data

Psychometrika, 2015
Missing data occur in many real world studies. Knowing the type of missing mechanisms is important for adopting appropriate statistical analysis procedure. Many statistical methods assume missing completely at random (MCAR) due to its simplicity. Therefore, it is necessary to test whether this assumption is satisfied before applying those procedures ...
Li, Jun, Yu, Yao
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Parameter identification forWiener‐finite impulse response system with output data of missing completely at random mechanism and time delay

International Journal of Adaptive Control and Signal Processing, 2021
SummaryIn this paper, the parameter estimation issue of Wiener system with random time delay and missing output data is studied. The linear part of Wiener system is described by Finite Impulse Response (FIR) model. The mathematical formula of the Expectation Maximum algorithm to identify Wiener‐FIR system that contains the random time delay and the ...
Qibing Jin, Zeyu Wang, Wu Cai, Yuming Zhang   +3 more
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Missing value imputation on missing completely at random data using multilayer perceptrons

Neural Networks, 2011
Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of ...
Esther-Lydia, Silva-Ramírez, Rafael, Pino-Mejías, Manuel, López-Coello, María-Dolores, Cubiles-de-la-Vega   +3 more
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Missing (Completely?) At Random: Lessons from Insurance Studies

Asia-Pacific Journal of Risk and Insurance, 2009
A dilemma frequently faced by empirical researchers is whether they should keep observations without complete information in the analysis. Assuming missingness is not biased in any perceivable direction, most studies use a complete case analysis approach, whereby only observations with complete information are kept for empirical estimation.
Jason J. H. Yeh
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A test of missing completely at random for generalised estimating equations with missing data

Biometrika, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Hua Yun, Little, Roderick
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Model averaging with covariates that are missing completely at random

Economics Letters, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xinyu Zhang
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The additive model affected by missing completely at random in the covariate

Computational Statistics, 2004
This paper focuses on a comparison of several imputation procedures within the simple additive model \(y=f(x)+\varepsilon\), where the independent variable \(x\) is affected by missing completely randomly. Such imputation methods as complete case analysis, mean imputation plus random noise, single imputation and two kinds of nearest neighbour ...
T. Nittner
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ARE HEALTH INSURANCE ITEM ALLOCATIONS IN THE AMERICAN COMMUNITY SURVEY MISSING COMPLETELY AT RANDOM?

Journal of Frailty & Aging, 2013
Background:Item allocation (the assignment of plausible values to missing or illogical responses insurvey studies) is at times necessary in the production of complete data sets. In the American Community Survey(ACS), missing responses to health insurance coverage questions are allocated. Objectives:Because allocationrates may vary
C. Siordia
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A Note on Normal Theory Power Calculation in SEM With Data Missing Completely at Random

Structural Equation Modeling: A Multidisciplinary Journal, 2005
We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muthen and Muthen (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of the (
Dolan, C., van der Sluis, S., Grasman, R.   +2 more
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