Results 101 to 110 of about 3,128,130 (383)
Computational and AI‐Driven Design of Hydrogels for Bioelectronic Applications
Advanced Electronic Materials, EarlyView.This review highlights the role of AI in advancing hydrogel design for bioelectronics, exploring natural, and synthetic gels tailored for applications like wound healing, biosensing, and tissue engineering. It emphasizes the synergy between hydrogels, electronics, and AI in creating responsive, multifunctional systems, showcasing recent innovations ...
Rebekah Finster +2 more
wiley +1 more source
IOP Conference Series: Earth and Environment, 2019 The Poisson regression is the popular regression model on the discrete response variables. The regression function in the Poisson regres-sion model can be estimate by using both the parametric and the nonparametric approaches.
Darnah, M. I. Utoyo, N. Chamidah
semanticscholar +1 more source
Applied Economic Perspectives and Policy, EarlyView.Abstract This paper employs machine learning to determine which preferential trade agreement (PTA) provisions are relevant to agricultural trade patterns and the factors that may influence their adoption. Utilizing the three‐way gravity model, we apply plug‐in Lasso regularized regression to pinpoint predictive PTA provisions for agricultural trade ...
Stepan Gordeev +3 more
wiley +1 more source
A miscellaneous note on the equivalence of two poisson likelihoods
, 2015 This note shows that the concept of an offset, frequently introduced in Poisson regression models to cope with ratetype data, can be simply treated with a regular Poisson regression model. Hence Poisson regression models requiring an offset can be fitted
Bohning, Dankmar +1 more
core +1 more source
American Journal of Orthodontics and Dentofacial Orthopedics, 2017 Despina, Koletsi, Nikolaos, Pandis
openaire +2 more sources
Asymptotic confidence intervals for Poisson regression
Journal of Multivariate Analysis, 2007 AbstractLet (X,Y) be a Rd×N0-valued random vector where the conditional distribution of Y given X=x is a Poisson distribution with mean m(x). We estimate m by a local polynomial kernel estimate defined by maximizing a localized log-likelihood function.
Michael Kohler, Adam Krzyżak
openaire +2 more sources
Efficient Bayesian inference for COM-Poisson regression models
Statistics and computing, 2018 COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and ...
Charalampos Chanialidis +3 more
semanticscholar +1 more source
Non‐Tariff Measures and U.S. Agricultural Exports
Applied Economic Perspectives and Policy, EarlyView.Abstract How much do non‐tariff measures (NTMs) affect U.S. agricultural exports? While countries maintain a large and diverse set of NTMs to safeguard the health of plants, animals, and humans, policymakers and regulatory bodies may neglect the impact these measures have on international trade.
Yunus Emre Karagulle +2 more
wiley +1 more source
A New Kibria-Lukman-Type Estimator for Poisson Regression Models
Acta InfologicaOne of the most important models for the analysis of count data is the Poisson Regression Model (PRM). The parameter estimates of the PRM are obtained by the Maximum Likelihood Estimator (MLE).
Cemal Çiçek, Kadri Ulaş Akay
doaj +1 more source
ZERO INFLATED NEGATIVE BINOMIAL MODELS IN SMALL AREA ESTIMATION [PDF]
, 2008 The problem of over-dispersion in Poisson data is usually solved by introducing prior distributions which lead to negative binomial models. Poisson data sometime is also suffered by excess zero problems, a condition when data contains too many zero or ...
Indahwati, Indahwati +2 more
core