Results 1 to 10 of about 29,166,992 (358)

Linear regression analysis study

Journal of the Practice of Cardiovascular Sciences, 2018
Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables.
Khushbu Kumari, Suniti Yadav
doaj   +2 more sources

Nonparametric regression analysis [PDF]

, 2015
textNonparametric regression uses nonparametric and flexible methods in analyzing complex data with unknown regression relationships by imposing minimum assumptions on the regression function.
Malloy, Shuling Guo
core   +1 more source

Multinomial Inverse Regression for Text Analysis [PDF]

, 2013
Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere.
Taddy, Matt
core   +1 more source

Regression analysis with categorized regression calibrated exposure: some interesting findings

Emerging Themes in Epidemiology, 2006
Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e ...
Hjartåker Anette, Laake Petter, Buonaccorsi John P, Dalen Ingvild, Thoresen Magne   +4 more
doaj   +1 more source

Regression analysis of ionospheric disturbance factors [PDF]

E3S Web of Conferences, 2020
Investigation of interactions of the near-planet space parameters, Earth magnetic field and ionospheric parameters are of interest in the tasks of solar-terrestrial physics and applied researches related to space weather.
Polozov Yuryi, Mandrikova Oksana
doaj   +1 more source

Semiparametric Regression Analysis via Infer.NET

Journal of Statistical Software, 2018
We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models.
Jan Luts, Shen S. J. Wang, John T. Ormerod, Matt P. Wand   +3 more
doaj   +1 more source

Bayesian analysis of a Tobit quantile regression model [PDF]

, 2007
This paper develops a Bayesian framework for Tobit quantile regression. Our approach is organized around a likelihood function that is based on the asymmetric Laplace dis- tribution, a choice that turns out to be natural in this context.
Stander, J, Yu, K
core   +1 more source

Tensor Regression with Applications in Neuroimaging Data Analysis [PDF]

, 2012
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors ...
Caffo B., Casey B., Davatzikos C., de Lathauwer L., de Leeuw J., de Leeuw J., Fan J., Frank I. E., Friston K. J., Hinrichs C., Hongtu Zhu, Hua Zhou, Hung H., Kang H., Kolda T. G., Lange K., Lazar N. A., Lexin Li, Li B., Li Y., Li Y., Lindquist M., Liu X., Martino F. D., McCullagh P., Park S. W., Polzehl J., Qiu P., Qiu P., Rao C. R., Reiss P., Rothenberg T. J., Ryali S., Sidiropoulos N. D., Sowell E. R., Tibshirani R., Valera E. M., van der Vaart A. W., Worsley K. J., Yue Y., Zhou H., Zou H.   +41 more
core   +3 more sources

Latent regression analysis [PDF]

Statistical Modelling, 2010
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups.
Tarpey, Thaddeus, Petkova, Eva
openaire   +4 more sources

Noncollapsibility and its role in quantifying confounding bias in logistic regression

BMC Medical Research Methodology, 2021
Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to ...
Noah A. Schuster, Jos W. R. Twisk, Gerben ter Riet, Martijn W. Heymans, Judith J. M. Rijnhart   +4 more
doaj   +1 more source