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The Impacts of Health and Environmental Information Nudges on Meat Choices: Where Does Goat Meat Fit?

Agribusiness, EarlyView.
ABSTRACT Amidst a recent surge in US goat meat imports to meet growing demand, this study contributes to the meat demand literature by examining consumer preferences for goat meat, a relatively healthy and environmentally friendly alternative to other popular meats.
Binod Khanal, Sunil P. Dhoubhadel, Rodolfo M. Nayga Jr.   +2 more
wiley   +1 more source

Reliability, bias, and computational cost of estimating the Bayes factor using bridge sampling and the Savage-Dickey density ratio. [PDF]

Behav Res Methods
Oberauer K, Musfeld P, Aust F.
europepmc   +1 more source

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Complementary Kernel Density Estimation

Pattern Recognition Letters, 2012
Generative models for vision and pattern recognition have been overshadowed in recent years by powerful non-parametric discriminative models. These discriminative models can learn arbitrary decision boundaries between classes and have proved very effective in classification and detection problems.
Xu Miao, Ali Rahimi, Rajesh P. N. Rao
openaire   +1 more source

Reweighted kernel density estimation

Computational Statistics & Data Analysis, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin L. Hazelton, Berwin A. Turlach
openaire   +2 more sources

On nonparametric kernel density estimates

Biometrika, 1990
SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
openaire   +1 more source

Estimating the Variance of a Kernel Density Estimation

2010
This article proposes an interval-valued extension of kernel density estimation. We show that the imprecision of this interval-valued estimation is highly correlated with the variance of the density estimation induced by the statistical variations of the set of observations.
Bilal Nehme, Olivier Strauss, Kevin Loquin   +2 more
openaire   +1 more source

Data Structures in Kernel Density Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985
We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included.
Luc Devroye, Fred Machell
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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Robust kernels for kernel density estimation

Economics Letters, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Shaoping, Li, Ang, Wen, Kuangyu, Wu, Ximing   +3 more
openaire   +2 more sources