Results 91 to 100 of about 56,202 (281)

Evaluating Estimator Performance Under Multicollinearity: A Trade-Off Between MSE and Accuracy in Logistic, Lasso, Elastic Net, and Ridge Regression with Varying Penalty Parameters

Stats
Multicollinearity in logistic regression models can result in inflated variances and yield unreliable estimates of parameters. Ridge regression, a regularized estimation technique, is frequently employed to address this issue.
H. M. Nayem, Sinha Aziz, B. M. Golam Kibria   +2 more
doaj   +1 more source

A computer intensive method for choosing the ridge parameter [PDF]

In this paper we describe a computer intensive method to find the ridge parameter in a prediction oriented linear model. With the help of a factorial experimental design the method is tested and compared to a classical one.
Czogiel, Irina, Lübke, Karsten, Weihs, Claus   +2 more
core  

High-Dimensional Inference: Confidence Intervals, $p$-Values and R-Software hdi

, 2015
We present a (selective) review of recent frequentist high-dimensional inference methods for constructing $p$-values and confidence intervals in linear and generalized linear models.
Bühlmann, Peter, Dezeure, Ruben, Meier, Lukas, Meinshausen, Nicolai   +3 more
core   +1 more source

Generalized Ridge Regression: Applications to Nonorthogonal Linear Regression Models

This paper analyzes the possibilities of using the generalized ridge regression to mitigate multicollinearity in a multiple linear regression model. For this purpose, we obtain the expressions for the estimated variance, the coefficient of variation, the coefficient of correlation, the variance inflation factor and the condition number.
Gómez, Román Salmerón, García, Catalina García, Reina, Guillermo Hortal   +2 more
openaire   +2 more sources

Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods

Hiroshima Mathematical Journal, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nagai, Isamu, Yanagihara, Hirokazu, Satoh, Kenichi   +2 more
openaire   +3 more sources

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors

Angewandte Chemie, EarlyView.
A universal catalyst design framework integrating weighted atom‐centered symmetry function (wACSF) descriptors with machine learning accurately predicts adsorption energies for 2e− water oxidation reaction. Microkinetic modeling and experimental validation confirm the framework's universality, establishing a powerful paradigm for rational ...
Zhijian Liu, Yan Liu, Yuqi Zhang, Yeyu Deng, Zhong Zheng, Ruth Knibbe, Tianxiang Gao, Mingzhe Li, Ziye Wang, Bingqian Zhang, Xue Jia, Di Zhang, Heng Liu, Xuqiang Shao, Zhengyang Gao, Li Wei, Hao Li, Weijie Yang   +17 more
wiley   +2 more sources

Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging [PDF]

, 2015
Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-subject neuroimaging data based on each subject's brain imaging scans.
Allen, Genevera I., Hu, Yue
core  

Generalized Ridge Regression Estimator in High Dimensional Sparse Regression Models

Statistics, Optimization & Information Computing, 2018
Modern statistical analysis often encounters linear models with the number of explanatory variables much larger than the sample size. Estimation in these high-dimensional problems needs some regularization methods to be employed due to rank deficiency of the design matrix.
openaire   +2 more sources

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source