Results 111 to 120 of about 272,931 (273)

Schichten mit geschätzten Schichtgrößen II

open access: yesAustrian Journal of Statistics, 2016
(Eine Weiterentwicklung und eine Korrektur zum Artikel des gleichen Autors im Heft 1/1996 der Österreichischen Zeitschrift für Statistik)
Andreas Quatember
doaj   +1 more source

Beyond the Ban—Shedding Light on Smallholders' Price Vulnerability in Indonesia's Palm Oil Industry

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT The Indonesian government imposed a palm oil export ban in April 2022 to address rising cooking oil prices. This study explores oil palm smallholders' vulnerability to the policy using descriptive statistics, Lasso, and post‐Lasso OLS regressions.
Charlotte‐Elena Reich   +3 more
wiley   +1 more source

Markovian acyclic directed mixed graphs for discrete data

open access: yes, 2014
Acyclic directed mixed graphs (ADMGs) are graphs that contain directed ($\rightarrow$) and bidirected ($\leftrightarrow$) edges, subject to the constraint that there are no cycles of directed edges.
Evans, Robin J., Richardson, Thomas S.
core   +1 more source

Climate Change Agricultural Comparative Advantage and the US Trade Balance

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT Current science indicates that warming and elevated atmospheric CO2 will have ambiguous results for crop productivity depending on crop type and geographic location, whereas increased heat stress makes livestock and human labor less productive.
Elizabeth A. Fraysse   +2 more
wiley   +1 more source

Robotic Versus Laparoscopic Anatomic Liver Resection: Comparison of Perioperative Outcomes—A Systematic Review and Meta‐Analysis

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Minimally invasive anatomic liver resection (AR) including major hepatectomy and liver parenchyma‐sparing AR is technically complex and demanding. This systematic review with meta‐analysis including 15 studies comparing 2042 robotic AR and 2129 laparoscopic AR patients demonstrated largely comparable perioperative outcomes and partly better outcomes ...
Yutaro Kato   +3 more
wiley   +1 more source

Posterior consistency of Gaussian process prior for nonparametric binary regression

open access: yes, 2007
Consider binary observations whose response probability is an unknown smooth function of a set of covariates. Suppose that a prior on the response probability function is induced by a Gaussian process mapped to the unit interval through a link function ...
Ghosal, Subhashis, Roy, Anindya
core   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

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

open access: yesAdvanced 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

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

open access: yesAdvanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia   +3 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

open access: yesAdvanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu   +4 more
wiley   +1 more source

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