Expectation-robust algorithm and estimating equations for means and dispersion matrix with missing data

Yuan, Ke‐Hai; Chan, Wai; Tian, Yubin

doi:10.1007/s10463-014-0498-1

Cited by 10 publications

(9 citation statements)

References 35 publications

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“…In the E-step, terms involving missing values in Equation 3 are replaced by their conditional expectations, and the R-step parallels to that in Equation 3, where cases that sit far from the center of the majority of observations will be downweighted. In our R package coefficientalpha, the implementation of the ER-algorithm with the Huber-type weights is based on Yuan et al (2015).…”

Section: Robust Estimation Of Covariance Matrixmentioning

confidence: 99%

“…In addition to the point estimator σ ^ , the output of the software also contains the SE of σ ^ and the corresponding CI for alpha. 6 The SE is based on the sandwich-type covariance matrix for the robust estimate boldΣ ^ , and is consistent regardless of the distribution of the sample (Yuan et al, 2015). It can also provide us the information on the efficiency of the robust method.…”

Section: Robust Coefficient Alphamentioning

confidence: 99%

“…It can also provide us the information on the efficiency of the robust method. The details of the ER-algorithm and the sandwich-type covariance matrix for boldΣ ^ are not the focus of the study, and are referred to Yuan et al (2015) and Yuan and Zhang (2012a). The SE for σ ^ based on the sandwich-type covariance matrix of boldΣ ^ is provided in Appendix A.…”

Section: Robust Coefficient Alphamentioning

confidence: 99%

“…The robustness of the developed procedure relies on the fact that outlying cases are downweighted in the estimation process. Missing data are handled by an expectation-robust (ER) algorithm (Yuan, Chan, & Tian, 2015), which is a generalization of the EM-algorithm based on a multivariate t distribution (Little, 1988). In the development, we assume that data are missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) but with auxiliary variables (e.g., Little & Rubin, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

Zhang

Yuan

2015

Educational and Psychological Measurement

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134

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Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.

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Section: Robust Estimation Of Covariance Matrixmentioning

confidence: 99%

Section: Robust Coefficient Alphamentioning

confidence: 99%

Section: Robust Coefficient Alphamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

Zhang

Yuan

2015

Educational and Psychological Measurement

Self Cite

134

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“…At present, a robust approach is widely used in various branches of mathematical statistics: factor analysis (Yuan and Zhong [4,5]; Toman [6]), discriminant analysis (Todorov [7]), claster analysis (Nevolainen [8]), regression models and multivariate analysis (Cizak [9]; Agostinelli [10]), general problems (Morgenthaler [11]; Toman, [6]; Mbaidheen and Alawneh [2]).…”

Section: Introductionmentioning

confidence: 99%

Project risk management in the construction of high-rise buildings

et al. 2018

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This paper shows the project risk management methods, which allow to better identify risks in the construction of high-rise buildings and to manage them throughout the life cycle of the project. One of the project risk management processes is a quantitative analysis of risks. The quantitative analysis usually includes the assessment of the potential impact of project risks and their probabilities. This paper shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Within the framework of the project risk management model a robust approach of P. Huber is applied and expanded for the tasks of regression analysis of project data. The suggested algorithms used to assess the parameters in statistical models allow to obtain reliable estimates. A review of the theoretical problems of the development of robust models built on the methodology of the minimax estimates was done and the algorithm for the situation of asymmetric “contamination” was developed.

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