Results 101 to 110 of about 24,401 (306)

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability

CPT: Pharmacometrics & Systems Pharmacology
We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose
Christos Kaikousidis, Robert R. Bies, Aristides Dokoumetzidis   +2 more
doaj   +1 more source

Orchestrating Green Transformation: How AI Adoption Enables Corporate Carbon Neutrality

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐
Xiaonan Dong, Sungjin Son
wiley   +1 more source

Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

Entropy, 2018
This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters.
M. Virtudes Alba-Fernández, M. Dolores Jiménez-Gamero, F. Javier Ariza-López   +2 more
doaj   +1 more source

Family Control and Ownership, Corporate Culture, and ESG Performance in Thailand

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Motivated by the growing importance of environmental, social, and governance (ESG) performance in emerging markets, we examine how family control and ownership, together with corporate culture, influence the ESG performance of publicly listed firms in Thailand.
Sirimon Treepongkaruna, Stefano Starita, Kam Fong Chan   +2 more
wiley   +1 more source

Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference

Risks
This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions.
Marcos Escobar-Anel, Yiyao Jiao
doaj   +1 more source

Structural Validity of a Short Form of the Eating Disorder Examination Questionnaire in Adolescents Diagnosed With an Eating Disorder

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective This study aimed to assess the structural validity and readability of a brief 7‐item version of the Eating Disorder Examination Questionnaire (the EDE‐Q7) among adolescents. Method Latent variable analysis was used in a sample of 263 adolescents (age range = 13–17.99y) diagnosed with an eating disorder and recruited from treatment ...
Paul E. Jenkins, Sasha Gorrell, Erin C. Accurso, Sarah Eckhardt, Elizabeth K. Hughes, Daniel Le Grange, Susan M. Sawyer, Renee D. Rienecke   +7 more
wiley   +1 more source

Uncertainty quantification for misspecified machine learned interatomic potentials

npj Computational Materials
The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which ...
Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov, Thomas D. Swinburne   +3 more
doaj   +1 more source

Convex Models, MLS and Misspecification

The Annals of Statistics, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

The Structure of Informal Learning in the Workplace—An Experience Sampling Approach

Human Resource Development Quarterly, EarlyView.
ABSTRACT This paper complements retrospective approaches to researching informal learning in the workplace with experience sampling. Since (conscious) informal learning is becoming increasingly important for successfully keeping pace with rapid changes in working environments, a clear understanding of the construct and its precise measurement are ...
Katja Häußermann, Tina Seufert
wiley   +1 more source

MULTIPARAMETRIC AND HIERARCHICAL SPATIAL AUTOREGRESSIVE MODELS: THE EVALUATION OF THE MISSPECIFICATION OF SPATIAL EFFECTS USING A MONTE CARLO SIMULATION

Acta Universitatis Lodziensis. Folia Oeconomica, 2014
The aim of this paper is to evaluate the spatial and hierarchical models for data generating processes with spatial heterogeneity and spatial dependence at the higher level.
Edyta Łaszkiewicz
doaj