Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocksOriginal Paper - Exploration Geophysics Open access Published: 20 April 2024 Volume 14, pages 1899–1921, (2024) Cite this article ...
What I've done in current paper is linear regression. Actually, it can realize what you have said in the quote: "showing by how much an increase in a unit of the independent variable, leading to an increase in a unit of the dependent variable (retweet)". We don't necessarily use Pois...
Linear regression is the machine learning task of uncovering the hidden linear relationship between the input and output data. In this chapter, we study linear regression from the ground up, laying the foundation for discussion of more complex nonlinear models in the chapters to come. 展开 ...
On the regression analysis of multivariate failure time data The paper is concerned with the analysis of regression effects when individual study subjects may experience multiple failures. The proposed methods are mo... PRENTICE R. L.,WILLIAMS B. J.,PETERSON A. V. - 《Biometrika》 被引量: ...
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student...
The generalized linear model has the same implementation as the general linear regression, pointed above, because the general linear model is just a special case. The generalized linear model allows applying a function on the output and that's about it....
The linear predictor was always a simple linear regression model, while the nonlinear predictor was the MMSE predictor for two-dimensional predictions (Fig. 4a–h) and the manifold-based predictor for higher-dimensional predictions (Fig. 4i,j). The MMSE predictor was as described above, except ...
This repo contains the code to reproduce the experimental results of https://arxiv.org/abs/1803.02596 - yuxiangw/optimal_dp_linear_regression
Locality-regularized linear regression classification (LLRC) is an effective classifier that shows great potential for face recognition. However, the original feature space cannot guarantee the classification efficiency of LLRC. To alleviate this problem, we propose a novel dimensionality reduction method ca...
The paper proposes a method for developing a linear model for Proportional Integral Derivative (PID) parameters of a PID controller. The approach is based on linear regression and it uses gradient descent algorithm for parameter optimization. A deep perusal of the literature shows that there have ...