scholarly journals Multiple Outlier Detection Tests for Parametric Models

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2156
Author(s):  
Vilijandas Bagdonavičius ◽  
Linas Petkevičius
Keyword(s):  
Normal Family ◽  
Asymptotic Properties ◽  
R Package ◽  
Parametric Models ◽  
Model Parameters ◽  
Outlier Identification ◽  
Identification Methods ◽  
Test Application ◽  
Scale Models ◽  
Z Scores

We propose a simple multiple outlier identification method for parametric location-scale and shape-scale models when the number of possible outliers is not specified. The method is based on a result giving asymptotic properties of extreme z-scores. Robust estimators of model parameters are used defining z-scores. An extensive simulation study was done for comparing of the proposed method with existing methods. For the normal family, the method is compared with the well known Davies-Gather, Rosner’s, Hawking’s and Bolshev’s multiple outlier identification methods. The choice of an upper limit for the number of possible outliers in case of Rosner’s test application is discussed. For other families, the proposed method is compared with a method generalizing Gather-Davies method. In most situations, the new method has the highest outlier identification power in terms of masking and swamping values. We also created R package outliersTests for proposed test.

scholarly journals Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package Ipcr

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 360-385
Author(s):  
Manuel Arnold ◽  
Andreas M. Brandmaier ◽  
Manuel C. Voelkle
Keyword(s):  
False Negative ◽  
R Package ◽  
Parametric Models ◽  
Alternative Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Modeling Framework ◽  
Data Set ◽  
Individual Parameter ◽  
Wide Range

Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation.

scholarly journals Analytical and Numerical Connections between Fractional Fickian and Intravoxel Incoherent Motion Models of Diffusion MRI

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1963
Author(s):  
Jingting Yao ◽  
Muhammad Ali Raza Anjum ◽  
Anshuman Swain ◽  
David A. Reiter
Keyword(s):  
Skeletal Muscle ◽  
Goodness Of Fit ◽  
Numerical Models ◽  
Tissue Perfusion ◽  
Parametric Models ◽  
Intravoxel Incoherent Motion ◽  
Fickian Diffusion ◽  
Model Parameters ◽  
Mathematical Framework ◽  
Stretched Exponential

Impaired tissue perfusion underlies many chronic disease states and aging. Diffusion-weighted imaging (DWI) is a noninvasive MRI technique that has been widely used to characterize tissue perfusion. Parametric models based on DWI measurements can characterize microvascular perfusion modulated by functional and microstructural alterations in the skeletal muscle. The intravoxel incoherent motion (IVIM) model uses a biexponential form to quantify the incoherent motion of water molecules in the microvasculature at low b-values of DWI measurements. The fractional Fickian diffusion (FFD) model is a parsimonious representation of anomalous superdiffusion that uses the stretched exponential form and can be used to quantify the microvascular volume of skeletal muscle. Both models are established measures of perfusion based on DWI, and the prognostic value of model parameters for identifying pathophysiological processes has been studied. Although the mathematical properties of individual models have been previously reported, quantitative connections between IVIM and FFD models have not been examined. This work provides a mathematical framework for obtaining a direct, one-way transformation of the parameters of the stretched exponential model to those of the biexponential model. Numerical simulations are implemented, and the results corroborate analytical results. Additionally, analysis of in vivo DWI measurements in skeletal muscle using both biexponential and stretched exponential models is shown and compared with analytical and numerical models. These results demonstrate the difficulty of model selection based on goodness of fit to experimental data. This analysis provides a framework for better interpreting and harmonizing perfusion parameters from experimental results using these two different models.

Combining Non-Parametric and Parametric Models for Stable and Computationally Efficient Battery Health Estimation

2020 ◽  
Author(s):  
Antti Aitio ◽  
David Howey
Keyword(s):  
Latent Variables ◽  
Linear Time ◽  
Full Range ◽  
Real Life ◽  
Internal Resistance ◽  
State Of Charge ◽  
Parametric Models ◽  
Model Parameters ◽  
Battery Management ◽  
Resistance Change

Abstract Equivalent circuit models for batteries are commonly used in electric vehicle battery management systems to estimate state of charge and other important latent variables. They are computationally inexpensive, but suffer from a loss of accuracy over the full range of conditions that may be experienced in real-life. One reason for this is that the model parameters, such as internal resistance, change over the lifetime of the battery due to degradation. However, estimating long term changes is challenging, because parameters also change with state of charge and other variables. To address this, we modelled the internal resistance parameter as a function of state of charge and degradation using a Gaussian process (GP). This was performed computationally efficiently using an algorithm [1] that interprets a GP to be the solution of a linear time-invariant stochastic differential equation. As a result, inference of the posterior distribution of the GP scales as 𝒪(n) and can be implemented recursively using a Kalman filter.

Parametric Models of Ultrasonic Piezoelectric Transducers over a Wide Temperature Range

2015 ◽  
Vol 2015 (HiTEN) ◽  
pp. 000266-000272 ◽  
Author(s):  
Steven A. Morris ◽  
Jeremy Townsend
Keyword(s):  
Dielectric Constant ◽  
Temperature Dependence ◽  
Coupling Coefficient ◽  
Thin Disk ◽  
Piezoelectric Transducers ◽  
Parametric Models ◽  
Model Parameters ◽  
Linear Temperature Dependence ◽  
Linear Temperature ◽  
Wide Range

Piezoelectric ultrasonic transducers are used extensively in well logging and logging-while-drilling applications for pulse-echo operation. We present a method of modeling the operation of ultrasonic thin-disk piezoelectric transducers over a wide range of temperatures. The model is based on using Redwood's version of Mason's model of thin-disk transducers. Laboratory measurements in the oven of non-backed transducers in air are used to extract the Mason model parameters as a function of temperature. Derived parameters are frequency-thickness constant, dielectric constant, and thickness mode coupling coefficient. A fourth parameter, bulk density, is measured independently and assumed constant over temperature. Temperature dependence of frequency thickness constant and coupling coefficient are modeled as linear temperature coefficients. Temperature dependence of the dielectric constant must be specified as a table because of the non-linear temperature dependence of that parameter.

scholarly journals BICORN: An R package for integrative inference of de novo cis-regulatory modules

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xi Chen ◽  
Jinghua Gu ◽  
Andrew F. Neuwald ◽  
Leena Hilakivi-Clarke ◽  
Robert Clarke ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Target Genes ◽  
De Novo ◽  
R Package ◽  
Model Parameters ◽  
Expression Data ◽  
Regulatory Modules ◽  
Genome Wide ◽  
Context Specific ◽  
Tf Gene

Abstract Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites, based on which cis-regulatory modules (CRMs) can be inferred. CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.

scholarly journals MARSHAL, a novel tool for virtual phenotyping of maize root system hydraulic architectures

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Félicien Meunier ◽  
Adrien Heymans ◽  
Xavier Draye ◽  
Valentin Couvreur ◽  
Mathieu Javaux ◽  
...  
Keyword(s):  
Root System ◽  
Web Application ◽  
Root Traits ◽  
Root Systems ◽  
R Package ◽  
Maize Root ◽  
Model Parameters ◽  
System Modelling ◽  
Architecture Model ◽  
System Models

Abstract Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly non-linear environment (soil, atmosphere), which makes the link between local traits and functioning unclear. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits is compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches. We describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations. To illustrate the tool’s potential, we generated contrasted maize hydraulic architectures that we compared with root system architectural and hydraulic observations. Observed variability of these traits was well captured by model ensemble runs. We also analysed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key model parameters to vary for virtual breeding. It is available as an R package, an RMarkdown pipeline and a web application.

Design for Thermo-Acoustic Stability: Modeling of Burner and Flame Dynamics

2013 ◽  
Author(s):  
Stefanie Bade ◽  
Michael Wagner ◽  
Christoph Hirsch ◽  
Thomas Sattelmayer ◽  
Bruno Schuermans
Keyword(s):  
Optimization Procedure ◽  
Parametric Models ◽  
Design Parameters ◽  
Model Parameters ◽  
Geometrical Parameters ◽  
Flame Dynamics ◽  
Stability Model ◽  
Flame Response ◽  
Burner Design ◽  
Acoustic Stability

A Design for Thermo-Acoustic Stability (DeTAS) procedure is presented, that aims at selecting a most stable burner geometry for a given combustor. It is based on the premise that a thermo-acoustic stability model of the combustor can be formulated and that a burner design exists, which has geometric design parameters that sufficiently influence the dynamics of the flame. Describing the flame dynamics in dependence of the geometrical parameters an optimization procedure involving a linear stability model of the target combustor maximizes the damping and thereby yields the optimal geometrical parameters. To demonstrate the procedure on an existing annular combustor a generic burner design was developed that features a significant variability of dynamical flame response in dependence of two geometrical parameters. In this paper the experimentally determined complex burner acoustics and complex flame responses are described in terms of physics based parametric models with excellent agreement between experimental and model data. It is shown that these model parameters correlate uniquely with the variation of the burner geometrical parameters, allowing to interpolate the model with respect to the geometrical parameters. The interpolation is validated with experimental data.

Testing and estimating change-points in time series

1985 ◽  
Vol 17 (4) ◽  
pp. 841-867 ◽  
Author(s):  
Dominique Picard
Keyword(s):  
Time Series ◽  
Asymptotic Distribution ◽  
Likelihood Ratio ◽  
Asymptotic Properties ◽  
Likelihood Ratio Tests ◽  
Parametric Models ◽  
The Other ◽  
Change Points ◽  
Analysis Of Time Series

The aim of this paper is to present a few techniques which may be useful in the analysis of time series when a failure is suspected. We present two categories of tests and investigate their asymptotic properties: one, of nonparametric type, is intended to detect a general failure in spectrum; the other investigates the properties of likelihood ratio tests in parametric models which have a non-standard behaviour in this situation. Finally, we obtain the asymptotic distribution of the likelihood estimators of the change parameters.

scholarly journals Variable selection for skew-normal mixture of joint location and scale models

2021 ◽  
Vol 36 (4) ◽  
pp. 475-491
Author(s):  
Liu-cang Wu ◽  
Song-qin Yang ◽  
Ye Tao
Keyword(s):  
Variable Selection ◽  
Model Parameters ◽  
Simulation Studies ◽  
Finite Sample ◽  
Normal Mixture ◽  
Explanatory Variables ◽  
Scale Models ◽  
Joint Location ◽  
Selection For ◽  
Skew Normal

AbstractAlthough there are many papers on variable selection methods based on mean model in the finite mixture of regression models, little work has been done on how to select significant explanatory variables in the modeling of the variance parameter. In this paper, we propose and study a novel class of models: a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The problem of variable selection for the proposed models is considered. In particular, a modified Expectation-Maximization(EM) algorithm for estimating the model parameters is developed. The consistency and the oracle property of the penalized estimators is established. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies. An example is illustrated by the proposed methodologies.

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