Results 71 to 80 of about 1,354,633 (198)

Longitudinal variable selection by cross-validation in the case of many covariates [PDF]

Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (Marginal Models, Mixed Effects Models, etc.), far fewer options appear to exist for the ...
Eva Cantoni, Chris Field, Joanna Mills Flemming, Elvezio Ronchetti   +3 more
core  

Three local search-based methods for feature selection in credit scoring

Vietnam Journal of Computer Science, 2018
Credit scoring is a crucial problem in both finance and banking. In this paper, we tackle credit scoring as a classification problem where three local search-based methods are studied for feature selection.
Dalila Boughaci, Abdullah Ash-shuayree Alkhawaldeh   +1 more
doaj   +1 more source

Variable Selection and Parameter Tuning in High-Dimensional Prediction [PDF]

, 2010
In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection.
Boulesteix, Anne-Laure, Bernau, Christoph   +1 more
core   +1 more source

Bayesian variable selection using Knockoffs with applications to genomics

, 2022
Given the costliness of HIV drug therapy research, it is important not only to maximize true positive rate (TPR) by identifying which genetic markers are related to drug resistance, but also to minimize false discovery rate (FDR) by reducing the number ...
Yap, JK, Gauran, IIM
core   +1 more source

Variable Selection in Additive Models by Nonnegative Garrote [PDF]

We adapt Breiman's (1995) nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no reliable distributional theory is available.
Eva Cantoni, Joanna Mills Flemming, Elvezio Ronchetti   +2 more
core  

Variable selection in Logistic regression model with genetic algorithm

, 2018
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often ...
Zhongheng Zhang, Sayed Shahabuddin Hoseini, Subha, Velappan, Victor Trevino, Trevino, Victor, Gorunescu, Florin, Velappan Subha, Boopathi, Arumugam Manivanna, Smaranda Belciug, Arumugam Manivanna Boopathi, Songshi Dai, Hoseini, Sayed Shahabuddin, Dai, Songshi, Ping Zhang, Zhang, Zhongheng, Belciug, Smaranda, Zhang, Ping, Florin Gorunescu   +17 more
core   +1 more source

Variable Selection in Time Series Forecasting Using Random Forests

Algorithms, 2017
Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored.
Hristos Tyralis, Georgia Papacharalampous   +1 more
doaj   +1 more source

High-dimensional variable selection

The Annals of Statistics, 2009
This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models.
Wasserman, Larry, Roeder, Kathryn
openaire   +5 more sources

Bayesian Model Averaging and Regularized Regression as Methods for Data-Driven Model Exploration, with Practical Considerations

Stats
Methodological experts suggest that psychological and educational researchers should employ appropriate methods for data-driven model exploration, such as Bayesian Model Averaging and regularized regression, instead of conventional hypothesis-driven ...
Hyemin Han
doaj   +1 more source

Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine

Journal of Research in Medical Sciences, 2019
Background: Large amounts of information have called for increased computational complexity. Data dimension reduction is therefore critical to preliminary analysis.
Saeedeh Pourahmad, Soheila Rasouli-Emadi, Fatemeh Moayyedi, Hosseinali Khalili   +3 more
doaj   +1 more source