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Estimation in nonlinear mixed-effects models using heavy-tailed distributions
2010
Statistics and computing
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Nonlinear mixed-effects models are very useful to analyze repeated measures data and are used in a variety of applications. ...
In this work, we introduce an extension of a normal nonlinear mixed-effects model considering a subclass of elliptical contoured distributions for both random effects and residual errors. ...
In Section 2, we describe the family of heavy-tailed distributions and EM and SAEM algorithms used in this work. Section 3 presents the nonlinear mixed-effects model with heavy-tailed distributions. ...
doi:10.1007/s11222-010-9212-1
fatcat:i6iqqs24wjb4pcxt5r2w53b75e
Bayesian estimation of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random components
2017
IOP Conference Series: Earth and Environment
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Mixed effects model and normal independent distributions
The normal mixed effects model In general, a Normal mixed effects model reads: ( ) (1) with ( ) ( ( )) where the subscript is the subject index ...
distribution for mixed effects model. ...
doi:10.1088/1755-1315/58/1/012006
fatcat:3gu73nfxzreilirfwcm7ma2wsq
Image denoising: a nonlinear robust statistical approach
2001
IEEE Transactions on Signal Processing
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Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated. ...
Nonlinear filtering techniques based on the theory of robust estimation are introduced. Some deterministic and asymptotic properties are derived. ...
Mean-LogCauchy Performance in Mixed Noise The scale-contaminated Gaussian and Laplace distributions are relatively light tailed. The distributions are very heavy-tailed noise distributions. ...
doi:10.1109/78.969512
fatcat:x6qqlhldqjh7lmnmhctikz46fq
Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies
2012
The International Journal of Biostatistics
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To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. ...
Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable ...
This may be due to the fact that an additional parameter ν for heaviness in the tails was estimated lessening the effect of skewness. ...
doi:10.1515/1557-4679.1387
pmid:22992288
pmcid:PMC4968403
fatcat:p3q5tez3mvfmbexl57tj4pdltq
Detecting multifractal stochastic processes under heavy-tailed effects
2014
Chaos, Solitons & Fractals
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A nonlinear estimated scaling function and non-trivial spectrum are usually considered as signs of a multifractal property in the data. ...
In this paper we analyze the method when the underlying process has heavy-tailed increments. ...
Summary and discussion We provided a rigorous proof that estimating the scaling function using the partition function can lead to nonlinear estimates under the presence of heavy tails. ...
doi:10.1016/j.chaos.2014.04.016
fatcat:d5a6kz4psnahldcbca342tuate
SS&IAGA-EM-based Algorithm for Fitting a Continuous PH Distribution
2011
Systems Engineering Procedia
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It is an important and difficult task in model analysis of traffic engineering to fit with the general distribution or test data whether which fit the distribution or not via PH distribution. ...
In the data fitting test for long-tailed distribution function, partial peak distribution function and heavytailed distribution function with a sample size of 104, the maximum error of the algorithm is ...
The copyright transfer covers the exclusive rights to reproduce and distribute the article, including reprints, photographic reproductions, microfilm or any other reproductions of similar nature and translations ...
doi:10.1016/j.sepro.2011.10.048
fatcat:ovu3q2tppfaqpjzctdq56w5w7q
Bayesian Approach to Nonlinear Mixed-Effects Quantile Regression Models for Longitudinal Data with Non-normality and Left-censoring
2016
Journal of Advanced Statistics
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variable conditional on covariates, which may lead to non-robust parameter estimates if obvious heavy tails exist as shown in Figure 1 (b). ...
First, in the literature, most studies of longitudinal modeling assume that model error and/or random-effects in mixed-effects models follow normal distribution due to mathematical tractability and computational ...
and/or heaviness in the tails. ...
doi:10.22606/jas.2016.13001
fatcat:ncjxkzez5fdgrhb2mw3xtf5anm
Multifractality distinguishes reactive from proactive cascades in postural control
[article]
2020
bioRxiv
pre-print
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The present work aims to situate the intermittency of dexterous behavior explicitly in multifractal modeling for non-Gaussian cascade processes. ...
Modeling orthogonal polynomials of non-Gaussianity's lambda-vs. ...
mixed-effects (LME) model using lmer a generalized linear mixed-effects (GLME) model fit changes in Lognormality as a dichotomous variable 512 (Lognormal = 1 vs. ...
doi:10.1101/2020.10.21.349589
fatcat:7hel6at74vglpoqpzrqeyfyw2e
Robust, Adaptive Functional Regression in Functional Mixed Model Framework
2011
Journal of the American Statistical Association
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generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets. ...
and the heaviness of the tails. ...
in setting of heavy-tailed distributions. ...
doi:10.1198/jasa.2011.tm10370
pmid:22308015
pmcid:PMC3270884
fatcat:xkzbko6xfngwxeaziqmr4dmt4i
Issues for the Next Generation of Health Care Cost Analyses
2009
Medical Care
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Many of the diagnostics used in choosing among alternatives have limitations that need more careful study. Several avenues in modeling cost data remain unexplored. ...
Objectives: We discuss the strengths and limitations in existing methods for estimation and for model specification and checking. ...
Traditional Single-Equation Models Traditional linear regression usually fails to model consistently and reliably the mean of a skewed distribution with a heavy right tail because of the nonlinearity in ...
doi:10.1097/mlr.0b013e31819c94a1
pmid:19536022
fatcat:rlyv6hwdwvb47imvg3qupstncq
Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models
2020
International Journal of Financial Research
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Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies ...
The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. ...
Second, this study finds generally that nonlinear GARCH-type models taking into account distributions of innovation that capture skewness, kurtosis and heavy tails constitute excellent tools in modelling ...
doi:10.5430/ijfr.v11n4p346
fatcat:hurhhpp47fhydfh4c3g5yprgxq
International diversification: A copula approach
2011
Journal of Banking & Finance
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In light of theoretical research linking diversification and dependence, we examine international diversification using two measures of dependence: correlations and copulas. ...
Second, there is evidence of asymmetric dependence or downside risk in the G5 and Latin America, but very little in east Asia. ...
Since copulas represent dependence of arbitrary distributions, in principle they allow us to examine diversification effects for heavy-tailed joint distributions, following the logic of Brumelle (1974 ...
doi:10.1016/j.jbankfin.2010.08.020
fatcat:pyevblqqqve23p5hbk5p3jvbsq
A New Adaptive Robust Unscented Kalman Filter for Improving the Accuracy of Target Tracking
2019
IEEE Access
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By using the robust filtering method to construct a new cost function used to modify the measurement covariance formula of the Kalman filter, the error of measurement model can be effectively suppressed ...
An adaptive filtering algorithm based on strong tracking idea is used in modifying the state equation of unscented Kalman filter (UKF), so that the algorithm can effectively improve the tracking ability ...
The error of the QS-ARUKF algorithm is smaller than that of other filters under the interference of process heavy tail, measurement heavy tail, and mixed Gaussian model. ...
doi:10.1109/access.2019.2921794
fatcat:vdrxqbn7hrf3njlcuiqpjnq33q
Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study
2013
PLoS ONE
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To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed ...
This article provides a fully Bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK) model. ...
The first nonlinear mixed-effects modeling program introduced for the analysis of large amounts of pharmacokinetic data is NONMEM [4] . ...
doi:10.1371/journal.pone.0058369
pmid:23520504
pmcid:PMC3592804
fatcat:yynbnwz5cbckdnriahdo352c3i
A robust approach to independent component analysis of signals with high-level noise measurements
2003
IEEE Transactions on Neural Networks
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In the second procedure, a nonlinear function is derived using the parameterized t-distribution density model. This nonlinear function is robust against the undue influence of outliers fundamentally. ...
By combining the t-distribution model with a family of light-tailed distributions (sub-Gaussian) model, we can separate the mixture of sub-Gaussian and super-Gaussian source components. ...
Endo, the National Institute of Bioscience and Human-Technology, Japan, for the AEF experiment and useful comments. ...
doi:10.1109/tnn.2002.806648
pmid:18238044
fatcat:rfiqfybumngmjggfmc5sybpjpm
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