Results 261 to 270 of about 2,028,100 (318)

Ordinal regression model and the linear regression model were superior to the logistic regression models

Journal of Clinical Epidemiology, 2006
Ordinal scales often generate scores with skewed data distributions. The optimal method of analyzing such data is not entirely clear. The objective was to compare four statistical multivariable strategies for analyzing skewed health-related quality of life (HRQOL) outcome data.
Colleen M, Norris, William A, Ghali, L Duncan, Saunders, Rollin, Brant, Diane, Galbraith, Peter, Faris, Merril L, Knudtson   +6 more
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REGRESSION, AUTOREGRESSION MODELS

Journal of Time Series Analysis, 1986
Abstract.The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of coefficients, of the innovations sequence and to the behaviour of the order (i.e., maximum lag) as determined by methods such as IAC, BIC.
Hannan, E. J., Kavalieris, L.
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Combined regression models

Computational Statistics, 2008
Three linear regression fitting criteria are considered: the least squates (LS), least absolute deviations (LSAD) and minimax (MM). The authors consider estimates which can be obtained by minimizing one of these criteria when some other (or both) are bounded by some fixed number.
Enrique F. Castillo, Carmen Castillo, Ali S. Hadi, José M. Sarabia   +3 more
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Regression Model Diagnostics

International Statistical Review / Revue Internationale de Statistique, 1992
Summary Various diagnostics for generalized linear models are reviewed and extended to more general models. These include some models for censored and grouped data, and regressions that are nonlinear, or where the response does not have an exponential family distribution.
Davison, A. C., Tsai, C.-L.
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The Multilevel Regression Model

2014
The multilevel regression model Social and behavioral research often concerns data that have a hierarchical structure, with individuals nested within groups. In multilevel analysis, such data structures are viewed as a multistage sample from a hierarchical population. For example, in educational research we may have a sample of schools, and within each
Hox, Joop, Wijngaards, Leoniek
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Regression Modeling Strategies

Revista Española de Cardiología (English Edition), 2011
Multivariable regression models are widely used in health science research, mainly for two purposes: prediction and effect estimation. Various strategies have been recommended when building a regression model: a) use the right statistical method that matches the structure of the data; b) ensure an appropriate sample size by limiting the number of ...
Nunez, E, Steyerberg, Ewout, Nunez, J
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Procrustus and the Regression Model: On the Misuse of the Regression Model

PS: Political Science & Politics, 1993
bility that some of the China specialists are government contractors, and their government ties may have contributed to the similarities in policy preferences. However, we still believe that the policy congruence is largely because of shared perceptions about cultural as well as strategic and political factors in China studies than links to the ...
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An uncertain regression model

Grey Systems: Theory and Application, 2011
PurposeThe purpose of this paper is to propose an uncertain regression model with an intrinsic error structure facilitated by an uncertain canonical process.Design/methodology/approachThis model is suitable for dealing with expert's knowledge ranging from small to medium size data of impreciseness.
Renkuan G. Guo, Danni Guo, Yanhong Cui
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Regression Model for Better Generalization and Regression Analysis

Proceedings of the 4th International Conference on Machine Learning and Soft Computing, 2020
Regression models such as polynomial regression when deployed for training on training instances may sometimes not optimize well and leads to poor generalization on new training instances due to high bias or underfitting due to small value of polynomial degree and may lead to high variance or overfitting due to high degree of polynomial fitting degree.
Mohiuddeen Khan, Kanishk Srivastava
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Equidistant regression modeling

Model Assisted Statistics and Applications, 2007
A criterion of equalizing residual squares is considered for regression modeling, and its results are compared with the ordinary least squares and least absolute deviations regression models. Equalized residuals yield a model with the minimum mean difference of the error squared or the most efficient residual variance.
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