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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

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

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

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

M-quantile regression analysis of temporal gene expression data [PDF]

, 2009
In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions.
Vinciotti, V, Yu, K
core   +1 more source

Advantages of the net benefit regression framework for trial-based economic evaluations of cancer treatments: an example from the Canadian Cancer Trials Group CO.17 trial. [PDF]

, 2019
BackgroundEconomic evaluations commonly accompany trials of new treatments or interventions; however, regression methods and their corresponding advantages for the analysis of cost-effectiveness data are not widely appreciated.MethodsTo illustrate ...
Chen, Bingshu E, Dewa, Carolyn S, Hay, Annette, Hoch, Jeffrey S, Isaranuwatchai, Wanrudee, Jonker, Derek, Leighl, Natasha B, Mittmann, Nicole, O'Callaghan, Chris, Pater, Joseph, Thavorn, Kednapa, Trenaman, Logan, Tu, Dongsheng   +12 more
core   +1 more source

Common pitfalls in statistical analysis: Linear regression analysis

Perspectives in Clinical Research, 2017
In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables.
Rakesh Aggarwal, Priya Ranganathan
doaj   +1 more source