Variables and Methods of Analysis
An Applied Comparison of Methods for Least- Squares Factor Analysis of Dichotomous Variables Charles D. H. Parry
... least-squares methods for factor analyzing dichotomous data (NOHARM, LISCOMP) did not outperform factor analysis using the tetrachoric correlation coefficients estimated from bivariate tables of the ...
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Complex Analysis Methods Related an Optimization Problem with Complex Variables
... 1 Department of Applied Mathematics, Chung Yuan Christian University, Taiwan 2 Niigata University, Japan Abstract. In this paper, we consider a nondifferentiable minimax fractional programming problem treated with ...
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Estimation methods in the errors-in-variables context
... spectral analysis, data mining, system identification, econometrics or time series analysis, the goal is often to identify or describe the internal laws that govern a system rather than to predict its ...
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ALARM DETECTION METHODS FOR PHYSIOLOGICAL VARIABLES
... Accordingly, the automatic control system incorporates an alarm system to estimate based on two different statistical methods: forecasting techniques and Walsh-Fourier spectral analysis. The ...
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Imputation Methods for Incomplete Dependent Variables in Finance
... explanatory variables (i.e., including too few variables in the missingness ...imputation methods have recently become ...explanatory variables (i.e., the MAR scheme), formal imputation ...
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Nonparametric methods for inference in the presence of instrumental variables
... [9] K RESS , R. (1999). Linear Integral Equations, 2nd ed. Springer, New York. MR1723850 [10] M ATHÉ , P. and P EREVERZEV , S. V. (1999). Optimal discretization and degrees of ill- posedness for inverse estimation in ...
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Dimensionality reduction methods for contingency tables with ordinal variables
... Correspondence analysis is a widely used tool for obtaining a graphical representa- tion of the interdependence between the rows and columns of a contingency table, and it is usually performed by applying a ...
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Methods for handling missing variables in risk prediction models
... Figure 1. Area under the receiver operating characteristic curve (AUC) (A) and Brier score (B) for each of the 6 modeling options for dealing with a missing predictor and with respect to the individual cohort left out ...
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Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables
... binary variables in terms of their importance for developing a medical out- come, which is a very common problem in variable importance ...art methods for causal inference to solve prediction and VIM ...
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Sensibility to noise of new multifractal fusion methods for ocean variables
... Singularity analysis is useful to track fronts or streamlines similarly to the methods described in Cayula and Cornillon (1995); Ullman and Cornillon ...singularity analysis methods and the ...
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variables to investigate Monte Carlo methods of t t production
... 1. Introduction 1.1. Top Quark Physics This report focuses an analysis of Monte Carlo simula- tions of a t¯ t decay in the ATLAS detector. The reason that such event are interesting is primarily due to the top ...
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On the Nuisance of Control Variables in Regression Analysis
... regression analysis is an important tool for empirical research in manage- ment, organization studies, and ...These methods account for confounding influence factors between a treatment and an outcome by ...
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Automated mapping of climatic variables using spatio-temporal geostatistical methods
... Vose, R. S., Division., O. R. N. L. E. S., (U.S.), G. C. R. P., of Energy. Office of Health, U. S. D., Research., E., (U.S.), C. D. I. A. C., and Martin Marietta Energy Systems, I. (1992). The Global historical ...
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Singularity analysis for heavy tailed random variables
... in the remaining sections. Steps 1 and 2 concern analytic extensions and notably use the Lindel¨ of and Bromwich integrals (Section 4). Steps 4 and 5 analyze the critical points of a bivariate function and deal with the ...
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Analysis of Stylometric Variables in Long and Short Texts
... Nowadays, stylometry has also incorporated Natural Language Processing (NLP) methods to explore different style markers based on syntactic analysis. In this paper, we combine both methods, ...
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Multiple regression analysis using climate variables
... data analysis. In terms of methods used, forward selection and stepwise always give the same results thus both are the best methods because these methods will strictly picked variables ...
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Bayesian Analysis for Errors in Variables with Changepoint Models
... random variables at which the underlying dis- tribution ...(MCMC) methods for changepoint with continuos ...MCMC methods for longitudinal data analysis in AIDS ...
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Computational methods for sums of random variables
... numerical methods also apply in a risk theory ...inversion methods have been employed, and compared to solve this particular problem in the work of Goffard et ...
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Scheduled Review Methods for Controllable State Variables
... review methods, called U 2 and U 3 , are introduced and the relative annual system costs are ...new methods results a significant reduction in the expected annual cost of the ...
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Lasso Methods for Gaussian Instrumental Variables Models
... For comparing standard errors, it is useful to remember that one can regard LASSO as a way to select variables in a situation in which there is no a priori information about which of the set of variables is ...
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