scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

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.

The analysis of binary longitudinal data with time-dependent covariates

2012 ◽  
Vol 31 (10) ◽  
pp. 931-948 ◽  
Author(s):  
Matthew W. Guerra ◽  
Justine Shults ◽  
Jay Amsterdam ◽  
Thomas Ten-Have
Keyword(s):  
Longitudinal Data ◽  
Time Dependent ◽  
Binary Longitudinal Data ◽  
Time Dependent Covariates

scholarly journals A mean-squared-error condition for weighting ionospheric delays in GNSS baselines

2021 ◽  
Vol 95 (11) ◽  
Author(s):  
P. J. G. Teunissen ◽  
A. Khodabandeh
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Present Contribution ◽  
Squared Error ◽  
Fixed Solution ◽  
Error Condition

AbstractAlthough ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify the needed weighting in dependence on ionospheric circumstances. The goal of the present contribution is therefore to develop and present the ionospheric conditions that need to be satisfied in order for the ionosphere-weighted solution to be mean squared error (MSE) superior to the ionosphere-float solution. When satisfied, the presented conditions guarantee from an MSE performance view, when (a) the ionosphere-fixed solution can be used, (b) the ionosphere-float solution must be used, or (c) an ionosphere-weighted solution can be used.

scholarly journals A new modified ridge-type estimator for the beta regression model: simulation and application

2021 ◽  
Vol 7 (1) ◽  
pp. 1035-1057
Author(s):  
Muhammad Nauman Akram ◽  
◽  
Muhammad Amin ◽  
Ahmed Elhassanein ◽  
Muhammad Aman Ullah ◽  
...  
Keyword(s):  
Regression Model ◽  
Mean Squared Error ◽  
Model Simulation ◽  
Beta Regression ◽  
Explanatory Variables ◽  
Squared Error ◽  
The Matrix ◽  
Biased Estimators ◽  
Highly Correlated ◽  
Beta Regression Model

<abstract> <p>The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.</p> </abstract>

scholarly journals Predicting the band gap of binary compounds from machine-learning regression methods

2021 ◽  
Author(s):  
Mengbo Guo ◽  
Xuyang Xu ◽  
Han Xie
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
Mean Squared Error ◽  
Gradient Boosting ◽  
Functional Theory ◽  
Regression Methods ◽  
Squared Error ◽  
State Band ◽  
Binary Compounds

Density functional theory (DFT) is a ubiquitous first-principles method, but the approximate nature of the exchange-correlation functional poses an inherent limitation for the accuracy of various computed properties. In this context, surrogate models based on machine learning have the potential to provide a more efficient and physically meaningful understanding of electronic properties, such as the band gap. Here, we construct a gradient boosting regression (GBR) model for prediction of the band gap of binary compounds from simple physical descriptors, using a dataset of over 4000 DFT-computed band gaps. Out of 27 features, electronegativity, periodic group, and highest occupied energy level exhibit the highest importance score, consistent with the underlying physics of the electronic structure. We obtain a model accuracy of 0.81 and root mean squared error of 0.26 eV using the top five features, achieving accuracy comparable to previously reported values but employing less number of features. Our work presents a rapid and interpretable prediction model for solid-state band gap with high fidelity to DFT and can be extended beyond binary materials considered in this study.

scholarly journals Parameter Estimation of Photovoltaic Models via Cuckoo Search

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jieming Ma ◽  
T. O. Ting ◽  
Ka Lok Man ◽  
Nan Zhang ◽  
Sheng-Uei Guan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Flight Behavior ◽  
Squared Error ◽  
Simulation Results ◽  
Bioinspired Algorithms ◽  
Significant Attention

Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE) value. The proposed method outperforms other algorithms applied in this study.

Using modified approaches on marginal regression analysis of longitudinal data with time-dependent covariates

2014 ◽  
Vol 33 (19) ◽  
pp. 3354-3364 ◽  
Author(s):  
Yi Zhou ◽  
John Lefante ◽  
Janet Rice ◽  
Shande Chen
Keyword(s):  
Regression Analysis ◽  
Longitudinal Data ◽  
Time Dependent ◽  
Marginal Regression ◽  
Time Dependent Covariates

scholarly journals Performances of LAD Regression, M-Regression and Quantile Regression Methods in order to Investigate Stock Prices of the Banks in the BIST Bank Index

2018 ◽  
Vol 6 (04) ◽  
Author(s):  
Umran Munire Kahraman ◽  
Neslihan Iyit
Keyword(s):  
Quantile Regression ◽  
Stock Prices ◽  
Robust Regression ◽  
Mean Squared Error ◽  
Stock Exchange ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Regression Methods ◽  
Istanbul Stock Exchange ◽  
Error Terms

In this study, performances of LAD regression, M-regression, Q25 and Q75 quantile regression models as robust regression methods alternative to the classical LS method are compared in the case of violations from the normality assumption of the error terms and the presence of an outlier. By using these alternative regression methods, stock prices of the 12 commercial banks and 1 participation bank listed in the Istanbul Stock Exchange (BIST) bank index between 2012 and 2016 are investigated in terms of equity size and equity profitability. As a result of this study, M-regression is the most suitable robust regression model with the smallest value of the mean squared error (MSE) measure and the small values for the standard errors of the parameter estimates belonging to the equity size and equity profitability. The smaller the standard errors of the parameter estimates, the narrower the resulting confidence intervals are obtained in M- regression. The accuracy as a measure of closeness of parameter estimates to the true values of the parameters is also obtained higher in M-regression.

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