LAUSR

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

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0 Published in 2017 at "Environmetrics"

DOI: 10.1002/env.2465

Abstract: It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this… read more here.

Keywords: model; model selection; mixture models; spatiotemporal multivariate ... See more keywords

Nonparametric variational learning of multivariate beta mixture models in medical applications

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0 Published in 2021 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22506

Abstract: Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering… read more here.

Keywords: variational learning; medical applications; beta mixture; mixture ... See more keywords

Development of visual predictive checks accounting for multimodal parameter distributions in mixture models

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0 Published in 2019 at "Journal of Pharmacokinetics and Pharmacodynamics"

DOI: 10.1007/s10928-019-09632-9

Abstract: The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a… read more here.

Keywords: multimodal parameter; visual predictive; mixture models; parameter distributions ... See more keywords

Deep Gaussian mixture models

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2 Published in 2019 at "Statistics and Computing"

DOI: 10.1007/s11222-017-9793-z

Abstract: Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, deep Gaussian mixture models (DGMM) are introduced and discussed. A DGMM… read more here.

Keywords: mixture; deep gaussian; mixture models; gaussian mixture ... See more keywords

Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

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0 Published in 2018 at "Ecological Modelling"

DOI: 10.1016/j.ecolmodel.2018.02.007

Abstract: Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic… read more here.

Keywords: bias; heterogeneity; fitting mixture; count data ... See more keywords

Zero-truncated panel Poisson mixture models: Estimating the impact on tourism benefits in Fukushima Prefecture.

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0 Published in 2018 at "Journal of environmental management"

DOI: 10.1016/j.jenvman.2017.12.082

Abstract: This study proposes an estimation approach to panel count data, truncated at zero, in order to apply a contingent behavior travel cost method to revealed and stated preference data collected via a web-based survey. We… read more here.

Keywords: zero truncated; mixture models; panel; poisson mixture ... See more keywords

Time-for-space substitution in N-mixture models for estimating population trends: a simulation-based evaluation

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0 Published in 2021 at "Scientific Reports"

DOI: 10.1038/s41598-021-84010-5

Abstract: N-mixture models usually rely on a meta-population design, in which repeated counts of individuals in multiple sampling locations are obtained over time. The time-for-space substitution (TSS) in N-mixture models allows to estimate population abundance and… read more here.

Keywords: time space; mixture models; abundance; mixture ... See more keywords

Flexible uncertainty in mixture models for ordinal responses

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0 Published in 2018 at "Journal of Applied Statistics"

DOI: 10.1080/02664763.2018.1555574

Abstract: ABSTRACT In classical mixture models for ordinal data with an uncertainty component, the Uniform distribution is used to model indecision. In the approach proposed here, the discrete Uniform distribution is replaced by a more flexible… read more here.

Keywords: models ordinal; uncertainty; model; mixture models ... See more keywords

Confidence limits for conformance proportions in normal mixture models

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1 Published in 2020 at "Journal of Applied Statistics"

DOI: 10.1080/02664763.2020.1769578

Abstract: Conformance proportions are important numerical indices for quality assessments. When the population is characterized by a normal mixture model, estimating conformance proportions can be a practical issue. To account for the inherent structure of normal… read more here.

Keywords: conformance; normal mixture; conformance proportions; mixture models ... See more keywords

Extensions of D-optimal minimal designs for symmetric mixture models

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1 Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2014.988258

Abstract: ABSTRACT The purpose of mixture experiments is to explore the optimum blends of mixture components, which will provide the desirable response characteristics in finished products. D-optimal minimal designs have been considered for a variety of… read more here.

Keywords: optimal minimal; symmetric mixture; mixture; extensions optimal ... See more keywords

A simple root selection method for univariate finite normal mixture models

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1 Published in 2018 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2018.1481972

Abstract: Abstract– It is well known that there exist multiple roots of the likelihood equations for finite normal mixture models. Selecting a consistent root for finite normal mixture models has long been a challenging problem. Simply… read more here.

Keywords: normal mixture; method; finite normal; root selection ... See more keywords