Comparison of regression and semi-empirical density
Quantile regression, VaR and CVAR. An empirical beta comparison of the techniques in relation to credit risk
... in comparison to their global peers, with a capital ratio that is about half that of US ...quantlile regression to non-performing assets and conduct a Beta analysis to measure fluctuations in credit risk, ...
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A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models
... Furthermore, we also test the accuracy capacity to detect the failure of a non-parametric statistical technique Classification/Regression Trees (CART), in comparison with the classic logistic ...
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A Comparison Study of Ridge Regression and Principle Component Regression with Application
... Ridge Regression; Principal Component ...linear regression model, and to fit the data under some assumptions with a single or multiple explanatory variables in order to choose the best regression ...
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Comparison between Modeling of Cetirizine Solubility Using Different Approaches: Semi Empirical Density Based Correlations vs Peng Robinson EoS
... approaches, semi-empir- ical density based correlations are well able to satisfactorily correlate the solubility of ...examined semi-empirical density based correlations correlation ...
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Regression models for analyzing radiological visual grading studies – an empirical comparison
... logistic regression methods are available for handling ordinal data from visual grad- ing experiments in medical ...any empirical support for selecting a different regression model than the one we ...
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Nonrandomly Missing Data in Multiple Regression Analysis: An Empirical Comparison of Ten Missing Data Treatments
... The influence of missing data on sample estimates of R 2 varied considerably among the missing data techniques across the conditions examined. In many instances, nonbiased parameter estimates were evidenced for the two ...
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A Comparison of Nonlinear. Regression Codes
... 5. Microsoft Excel 6. Minpack The Statistical Reference Datasets Project was developed by the Statistical Engineering Division and the Computational Sciences Division within the Information Technology Laboratory of the ...
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Nonparametric comparison of regression functions
... © 2010 Elsevier Inc. All rights reserved. 1. Introduction In many applied fields, e.g., health science, engineering, agriculture or medicine, it has always been of interest to choose between two complementary courses of ...
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A Comparison of the Lasso and Marginal Regression
... marginal regression yields exact recovery with 50 repetition of simulations, respectively (similarly, the tuning parameters of each method are set ...marginal regression faces a so-called challenge of ...
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Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio
... Poisson regression, however, can also present problems when the estimates are on the boundaries of the parameter space, as mentioned ...logistic regression and robust Poisson ...Poisson regression, ...
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Forecast comparison of principal component regression and principal covariate regression
... covariates, regression model, factor model, economic forecasting 1 Introduction In many forecasting applications in macroeconomics and finance, a large num- ber of predictor variables are available that may all ...
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Semi-Parametric Hedonic Models, and Empirical Comparison
... The main advantage of non-parametric methods is that they allow the data to determine which is the most appropriate functional form instead of impos- ing it a priori (Racine and Ullah 2006). As a result, possible ...
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Density Regression Based on Proportional Hazards Family
... Gt-PH regression method and make some comparison with MLE-based inference, which we focus on finite sample performance of estimators of regression parameter β and G-parameter ...
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Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction
... of regression for three parameters - the zenith angle, and the azimuth angle is transformed into Euclidean x,y ...in comparison to KRR, which demonstrates the effectiveness of semi-supervised ...
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Distributed Semi-supervised Learning with Kernel Ridge Regression
... In this section, we report experimental studies to justify the statements in Section 2. We employ two criteria for the comparison. The first criterion is the global error (GE) which is the mean square error of a ...
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Advances in Semi-Nonparametric Density Estimation and Shrinkage Regression
... the density functions of continuous random vectors, which makes use of a base density function and a moment- based polynomial ...base density function, one can directly obtain density ...
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Nonparametric circular methods for density and regression
... CircSiZer map with pointwise confidence intervals is that some features may be identified as sig- nificant when they are not. The pointwise coverages of normal and bootstrap confidence intervals have been studied for the ...
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Two Stage Semi Parametric Quantile Regression
... Kernel regression estimator whose bias is inversely related to the density of the condi- tioning ...Kernel regression(Ruppert and Wand 1994)for estimating our nuisance functions would not have this ...
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Semi-parametric bivariate polychotomous ordinal regression
... a semi-parametric estimation of the covariate-response ...Moreover, semi-parametric modelling avoids categorising continuous variables into groups based on intervals or ...in empirical studies, is ...
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Empirical likelihood for regression discontinuity design
... The second issue that has attracted researchers’ attention is the importance of nonparametric meth- ods in RDD analysis (e.g. Sacks and Ylvisaker, 1978, Knafl, Sacks and Ylvisaker, 1985). Since RDD analysis is concerned ...
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