Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia's Hospital [PDF]
, 2015 Fuzzy linear regression analysis has become popular among researchers and standard model in analysing data vagueness phenomena. These models were represented by five statistical models such as multiple linear regression, fuzzy linear regression (Tanaka),
Ahmad Hilmi Azman +7 more
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a multiple linear regression model [PDF]
, 2015 The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month.
Bissolli, Peter +3 more
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Efficient semiparametric estimation of a partially linear quantile regression model [PDF]
, 2003 This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear. The paper develops a simple estimator of the parametric component of the conditional quantile.
Lee, S
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On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors [PDF]
, 2017 Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome.
Papathomas, Michail
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Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model
Journal of Probability and Statistics, 2020 Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the ...
Manickavasagar Kayanan +1 more
doaj +1 more source
An approach to select linear regression model in bioanalytical method validation
Journal of Analytical Science and Technology, 2019 BackgroundThe accuracy of any bioanalytical method depends on the selection of an appropriate calibration model. The most commonly used calibration model is the unweighted linear regression, where the response (y-axis) is plotted against the ...
S. Sonawane +3 more
semanticscholar +1 more source
Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality [PDF]
Journal of The Royal Statistical Society Series B-statistical Methodology, 2020 This paper considers estimation and prediction of a high‐dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models ...
Sai Li, T. Cai, Hongzhe Li
semanticscholar +1 more source
Re-sampling in Linear Regression Model Using Jackknife and Bootstrap [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic.
Zakariya Y. Algamal, Khairy B. Rasheed
doaj +1 more source
Validating linear restrictions in linear regression models with general error structure [PDF]
, 2006 A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability.
Czado, Claudia +2 more
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Robust Estimation and Wavelet Thresholding in Partial Linear Models [PDF]
, 2006 This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors.
Gannaz, Irène
core +5 more sources