Results 41 to 50 of about 79,637 (281)
Fit assessment and selection between competitive models in sem
Statistica, 2007 This paper outlines some issues in fit assessment and in selection between competitive models in Structural Equation Models(SEM). The theory on chi-square statistic and the results of some simulation studies are reviewed and the methodological ...
Roberto Di Natale
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Identifying the sources of model misspecification [PDF]
Journal of Monetary Economics, 2020 The first and third authors acknowledge financial support from the National Science Foundation through grants 102159 and 1022125, respectively.
Atsushi Inoue +2 more
openaire +7 more sources
How Competitive Is Myanmar's Rice Sector? A Comparison of Production Costs and Efficiency
Agribusiness, EarlyView.ABSTRACT This paper analyzes the cost competitiveness of rice production in Myanmar by examining production costs, cost efficiency, and the potential effect of improving cost efficiency on the country's global competitiveness. To achieve this, we conduct a comparative analysis of production costs among major rice‐producing countries and estimate the ...
Nandar Aye Chan +3 more
wiley +1 more source
Agribusiness, EarlyView.ABSTRACT The origin of a product, if associated with good quality, can contribute to building a positive collective reputation, leading to a potential price premium. However, it is conceivable that a producer markets a product by evoking symbols, images, words, and values typical of places other than where it was designed or produced, creating a ...
Annalisa Caloffi +2 more
wiley +1 more source
Parameter uncertainties for imperfect surrogate models in the low-noise regime
Machine Learning: Science and TechnologyBayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly ...
Thomas D Swinburne, Danny Perez
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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
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Eco‐Innovation: Drivers and Obstacles for Agriculture Firms in a Developing Country
Business Strategy and the Environment, EarlyView.ABSTRACT Sustainable agriculture is becoming increasingly vital for food security and economic growth, particularly as the agricultural sector grapples with challenges posed by climate change. This study, grounded in the environmental innovation theory, resource‐based view, and dynamic capabilities approach, uses logistic and multinomial logistic ...
Nelyda Campos‐Requena +4 more
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European Ports as Energy Hubs: A Sustainability Index to Assess Territorial Development
Business Strategy and the Environment, EarlyView.ABSTRACT The capacity of ports to act as energy hubs is increasingly relevant in light of sustainability challenges and global crises like the Ukraine conflict, the COVID‐19 pandemic and energy scarcity. Numerous international and national initiatives are shaping the future of port development to address environmental and economic concerns.
Paolo Mazzocchi +3 more
wiley +1 more source
European Journal of Personality, EarlyView., 2020 Abstract Personality can be described at different levels of abstraction. Whereas the Big Five domains are the dominant level of analysis, several researchers have called for more fine‐grained approaches, such as facet‐level analysis. Personality facets allow more comprehensive descriptions, more accurate predictions of outcomes, and a better ...
Daniel Danner +4 more
wiley +1 more source
Can quartet analyses combining maximum likelihood estimation and Hennigian logic overcome long branch attraction in phylogenomic sequence data? [PDF]
PLoS ONE, 2017 Systematic biases such as long branch attraction can mislead commonly relied upon model-based (i.e. maximum likelihood and Bayesian) phylogenetic methods when, as is usually the case with empirical data, there is model misspecification.
Patrick Kück +4 more
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