Steady-state quantile parameter estimation: An empirical comparison of stochastic kriging and quantile regression

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

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Parameter estimation through semiparametric quantile regression imputation

Electronic Journal of Statistics ◽

10.1214/16-ejs1208 ◽

2016 ◽

Vol 10 (2) ◽

pp. 3621-3647 ◽

Cited By ~ 2

Author(s):

Senniang Chen ◽

Cindy L. Yu

Keyword(s):

Parameter Estimation ◽

Quantile Regression ◽

Regression Imputation

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Parameter Selection in the Parameter Estimation of Grade Transitions in a Polyethylene Plant

Studies in Engineering and Technology ◽

10.11114/set.v3i1.887 ◽

2015 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

Niklas Andersson ◽

Per-Ola Larsson ◽

Johan Åkesson ◽

Niclas Carlsson ◽

Staffan Skålén ◽

...

Keyword(s):

Parameter Estimation ◽

Steady State ◽

State Parameter ◽

Parameter Selection ◽

Selection Algorithm ◽

Parameter Estimations ◽

Dynamic Case ◽

Nominal Parameter ◽

Best Parameter ◽

Parameter Values

A polyethylene plant at Borealis AB is modelled in the Modelica language and considered for parameter estimations at grade transitions. Parameters have been estimated for both the steady-state and the dynamic case using the JModelica.org platform, which offers tools for steady-state parameter estimation and supports simulation with parameter sensitivies. The model contains 31 candidate parameters, giving a huge amount of possible parameter combinations. The best parameter sets have been chosen using a parameter-selection algorithm that identified parameter sets with poor numerical properties. The parameter-selection algorithm reduces the number of parameter sets that is necessary to explore. The steady-state differs from the dynamic case with respect to parameter selection. Validations of the parameter estimations in the dynamic case show a significant reduction in an objective value used to evaluate the quality of the solution from that of the nominal reference, where the nominal parameter values are used.

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