Results 121 to 130 of about 7,721,766 (241)
Random Covariance Heterogeneity in Discrete Choice Models [PDF]
The area of discrete choice modelling has developed rapidly in recent years. In particular, continuing refinements of the Generalised Extreme Value (GEV) model family have permitted the representation of increasingly complex patterns of substitution and ...
Stephane Hess, John Polak, Denis Bolduc
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A new class of nonseparable space-time covariance models [PDF]
, 2008 The aim of this work is to construct nonseparable, stationary covariance functions for processes that vary continuously in space and time. Stochastic modeling of phenomena over space and time is important in many areas of applications such as ...
Fonseca, Thaís C. O., Steel, Mark F. J.
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A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines
Advanced Intelligent Systems, EarlyView.(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe +9 more
wiley +1 more source
Unbiased covariance estimation with interpolated data [PDF]
We study covariance estimation when compelled to use evenly spaced data which have already been manipulated by previous-tick interpolation. We propose an un- biased covariance estimator, which is designed to correct for the two biases arising because of ...
Taro Kanatani, Roberto Reno'
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American Journal of Agricultural Economics, EarlyView.Abstract Germany's Renewable Energy Sources Act (REA), enacted in 2000 and subsequently amended, subsidized national renewable energy production with fixed feed‐in tariffs for renewable energy sources (RE) from wind, solar, and biogas. Empirical studies suggest that the policy was creating windfall effects for landowners and attribute farmland use ...
Lars Isenhardt +6 more
wiley +1 more source
The Neurobiological Correlates of Structural Covariance Networks
, 2022 There exist many studies examining population-level correlations between the cortical thickness or volumes of brain structures. Yet, the reasons for why these correlations—termed structural covariance—form are unclear.
Yee, Yohan
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American Journal of Agricultural Economics, EarlyView.Abstract This study develops and empirically estimates a structural framework to decompose the causal pathways of multilevel behavioral interventions targeting adolescent health behaviors. We apply this framework to the Kids SIPsmartER (KSS) program, a 6‐month, school‐based intervention evaluated through a clustered randomized controlled trial in rural
Naveen Abedin +5 more
wiley +1 more source
On relative efficiency of Quasi-MLE and GMM estimators of covariance structure models [PDF]
Optimal GMM is known to dominate Gaussian QMLE in terms of asymptotic efficiency (Chamberlain, 1984). I derive a new condition under which QMLE is as efficient as GMM for a general class of covariance structure models.
Artem Prokhorov
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Structural covariance refers to the concurrent changes in one morphological measure between two brain regions. Structural covariance of cortical morphological measures such as cortical thickness (CT), surface area (SA), and cortical volume (CV) have been
Ailin Hou +5 more
core +1 more source
International Forum of Allergy &Rhinology, EarlyView.ABSTRACT Background Chronic rhinosinusitis (CRS) is frequently associated with polymicrobial biofilms involving Staphylococcus aureus and Pseudomonas aeruginosa. Interactions between these organisms are thought to influence disease severity, but the epithelial effects of exoproteins derived from patient‐matched cocultures remain poorly defined ...
Xiaohan Sun +6 more
wiley +1 more source