Joint Estimation of Model Parameters and Outlier Effects in Time Series

1993 ◽  
Vol 88 (421) ◽  
pp. 284 ◽  
Author(s):  
Chung Chen ◽  
Lon-Mu Liu
Keyword(s):  
Time Series ◽  
Joint Estimation ◽  
Model Parameters ◽  
Estimation Of Model Parameters

scholarly journals Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters

2020 ◽  
Vol 36 (10) ◽  
pp. 3169-3176 ◽  
Author(s):  
Akio Onogi
Keyword(s):  
Mathematical Models ◽  
R Package ◽  
Joint Estimation ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Statistical Framework ◽  
Genome Wide ◽  
Estimation Of Model Parameters

Abstract Motivation Parameters of mathematical models used in biology may be genotype-specific and regarded as new traits. Therefore, an accurate estimation of these parameters and the association mapping on the estimated parameters can lead to important findings regarding the genetic architecture of biological processes. In this study, a statistical framework for a joint analysis (JA) of model parameters and genome-wide marker effects on these parameters was proposed and evaluated. Results In the simulation analyses based on different types of mathematical models, the JA inferred the model parameters and identified the responsible genomic regions more accurately than the independent analysis (IA). The JA of real plant data provided interesting insights into photosensitivity, which were uncovered by the IA. Availability and implementation The statistical framework is provided by the R package GenomeBasedModel available at https://github.com/Onogi/GenomeBasedModel. All R and C++ scripts used in this study are also available at the site. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estimation with Aggregate Shocks

2019 ◽  
Vol 87 (3) ◽  
pp. 1365-1398 ◽  
Author(s):  
Jinyong Hahn ◽  
Guido Kuersteiner ◽  
Maurizio Mazzocco
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Model Parameters ◽  
Test Statistics ◽  
Cross Sectional ◽  
Aggregate Shocks ◽  
Combined Use ◽  
The Cross ◽  
Estimation Of Model Parameters

Abstract Aggregate shocks affect most households’ and firms’ decisions. Using three stylized models, we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents’ decision problem relative to aggregate shocks. Our framework and examples illustrate that the cross-sectional and time-series aspects of the model are often interdependent. Therefore, estimation of model parameters in the presence of aggregate shocks requires the combined use of cross-sectional and time-series data. We provide easy-to-use formulas for test statistics and confidence intervals that account for the interaction between the cross-sectional and time-series variation. Lastly, we perform Monte Carlo simulations that highlight the properties of the proposed method and the risks of not properly accounting for the presence of aggregate shocks.

ESTIMATION OF MODEL PARAMETERS FROM TIME SERIES BY OPCL AUTO-SYNCHRONIZATION

2004 ◽  
Vol 14 (06) ◽  
pp. 2133-2141 ◽  
Author(s):  
IOAN GROSU
Keyword(s):  
Time Series ◽  
Global Minimum ◽  
Error Function ◽  
Numerical Results ◽  
Model Parameters ◽  
Estimation Of Model Parameters ◽  
Determination Of Parameters

OPCL autosynchronization ([1997] Phys. Rev.E56, 3709–3711) is used for determination of parameters in 1-D models. Numerical results are given for noisy Duffing and logistic systems. The proposed algorithm works also for models that contain parameters nonlinearly. Numerical results show that the error function has a global minimum.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

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