Results 151 to 160 of about 284,254 (315)

Serum Bile Acids Are Useful Indicators of Intrahepatic Recurrence After Initial Curative Hepatectomy for Hepatocellular Carcinoma

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Aim Bile acids accumulation in hepatocytes causes liver damage and contributes to the development of hepatocellular carcinoma. However, the association between serum bile acid levels and postoperative intrahepatic recurrence in hepatocellular carcinoma remains unclear.
Tomoaki Bekki, Masahiro Ohira, Yuki Imaoka, Seiichi Simizu, Tomoyuki Abe, Shintaro Kuroda, Hiroyuki Tahara, Kentaro Ide, Tsuyoshi Kobayashi, Hideki Ohdan   +9 more
wiley   +1 more source

Nonparametric regression method with functional covariates and multivariate response

, 2017
Kurdistan M. Taher Omar, Bo Wang
openalex   +2 more sources

Nonparametric principal subspace regression [PDF]

, 2019
Mark Koudstall, Dengdeng Yu, Dehan Kong, Fang Yao   +3 more
openalex   +1 more source

Application of Nonparametric Geographically Weighted Spline Regression Model for Spatial Mapping of Open Unemployment Rate in Kalimantan

, 2021
Sifriyani Sifriyani, Hillidatul Ilmi, Zakiyah Mar’ah   +2 more
openalex   +1 more source

A Truncated Spline and Local Linear Mixed Estimator in Nonparametric Regression for Longitudinal Data and Its Application [PDF]

, 2022
Idhia Sriliana, I Nyoman Budiantara, Vita Ratnasari   +2 more
openalex   +1 more source

Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically [PDF]

We introduce a new kernel smoother for nonparametric regression that uses prior information on regression shape in the form of a parametric model. In effect, we nonparametrically encompass the parametric model. We derive pointwise and uniform consistency
Oliver Linton, Pedro Gozalo
core  

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Nonparametric modal regression [PDF]

, 2016
Yen‐Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani, Larry Wasserman   +3 more
openalex   +1 more source

Dynamic Misspecification in Nonparametric Cointegrating Regression [PDF]

Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration.
Ioannis Kasparis, Peter C.B. Phillips
core  

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source