Results 261 to 270 of about 284,254 (315)
A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines. [PDF]
Front Math China, 2013 Zhu W, Zhang H.
europepmc +1 more source
Epilepsia Open, EarlyView.Abstract Objective To characterize the neuropsychological profile and social cognition, particularly Theory of Mind (ToM), in 45 children and adolescents with temporal lobe epilepsy (TLE) compared with 56 age, gender, and schooling‐matched healthy controls.
Claudia Accolla +5 more
wiley +1 more source
New Local Estimation Procedure for Nonparametric Regression Function of Longitudinal Data. [PDF]
J R Stat Soc Series B Stat Methodol, 2013 Yao W, Li R.
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Asymptotic Equivalence for Nonparametric Regression [PDF]
Ion Grama, Michael Nussbaum
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A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, EarlyView.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
Journal of Forecasting, EarlyView.ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann +2 more
wiley +1 more source
Improved cardiovascular risk prediction using nonparametric regression and electronic health record data. [PDF]
Med Care, 2013 Kennedy EH +3 more
europepmc +1 more source
NONPARAMETRIC REGRESSION ON FUNCTIONAL DATA: INFERENCE AND PRACTICAL ASPECTS
, 2007 Frédéric Ferraty +2 more
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Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
Journal of Forecasting, EarlyView.ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
wiley +1 more source