scholarly journals ROBUST ESTIMATORS FOR CIRCULAR REGRESSION MODELS

2021 ◽  
pp. 101576
Author(s):  
SHOKRYA ALSHQAQ ◽  
ALI ABUZAID ◽  
ABDULLAH AHMADINI
Keyword(s):  
Regression Models ◽  
Robust Estimators ◽  
Circular Regression

scholarly journals GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS

2008 ◽  
Vol 24 (6) ◽  
pp. 1500-1529 ◽  
Author(s):  
Pavel Čížek
Keyword(s):  
Regression Models ◽  
Breakdown Point ◽  
Robust Estimators ◽  
Linear Regression Models ◽  
Mixing Conditions ◽  
Least Trimmed Squares ◽  
Limited Dependent Variable ◽  
Data Contamination ◽  
High Breakdown Point ◽  
Regression Estimators

High-breakdown-point regression estimators protect against large errors and data contamination. We generalize the concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which renders robust estimators applicable far beyond the standard (non)linear regression models. We derive here the consistency and asymptotic distribution of the proposed general trimmed estimator under mild β-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.

scholarly journals On the least trimmed squares estimators for JS circular regression model

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Shokrya Saleh Alshqaq ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Real Data ◽  
Linear Regression Models ◽  
Least Trimmed Squares ◽  
Leverage Points ◽  
Circular Regression ◽  
Robust Linear Regression

The least trimmed squares (LTS) estimation has been successfully used in the robust linear regression models. This article extends the LTS estimation to the Jammalamadaka and Sarma (JS) circular regression model. The robustness of the proposed estimator is studied and the used algorithm for computation is discussed. Simulation studied, and real data show that the proposed robust circular estimator effectively fits JS circular models in the presence of vertical outliers and leverage points.

scholarly journals Assessing a Bayesian Embedding Approach to Circular Regression Models

Methodology ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 69-81 ◽  
Author(s):  
Jolien Cremers ◽  
Tim Mainhard ◽  
Irene Klugkist
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Regression Models ◽  
Sampling Method ◽  
Circular Data ◽  
Interpersonal Circumplex ◽  
Simulation Studies ◽  
Mcmc Sampling ◽  
Circular Regression

Abstract. Circular data is different from linear data and its analysis also requires methods different from conventional methods. In this study a Bayesian embedding approach to estimating circular regression models is investigated, by means of simulation studies, in terms of performance, efficiency, and flexibility. A new Markov chain Monte Carlo (MCMC) sampling method is proposed and contrasted to an existing method. An empirical example of a regression model predicting teachers’ scores on the interpersonal circumplex will be used throughout. Performance and efficiency are better for the newly proposed sampler and reasonable to good in most situations. Furthermore, the method in general is deemed very flexible. Additional research should be done that provides an overview of what circular data looks like in practice, investigates the interpretation of the circular effects and examines how we might conduct a way of hypothesis testing or model checking for the embedding approach.

scholarly journals A mixture of linear-linear regression models for a linear-circular regression

2019 ◽  
pp. 1471082X1988184
Author(s):  
Ali Esmaieeli Sikaroudi ◽  
Chiwoo Park
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Likelihood Estimation ◽  
Estimation Accuracy ◽  
Nonparametric Model ◽  
Linear Regression Models ◽  
Regression Problem ◽  
Estimation Algorithms ◽  
Circular Regression ◽  
Wrapped Normal

We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modelling approach of a wrapped normal distribution that describes angular variables and angular distributions and advances them for a linear-circular regression analysis. Some previous works model a circular variable as projection of a bivariate Gaussian random vector on the unit square, and the statistical inference of the resulting model involves complicated sampling steps. The proposed model treats circular responses as the result of the modulo operation on unobserved linear responses. The resulting model is a mixture of multiple linear-linear regression models. We present two EM algorithms for maximum likelihood estimation of the mixture model, one for a parametric model and another for a nonparametric model. The estimation algorithms provide a great trade-off between computation and estimation accuracy, which was numerically shown using five numerical examples. The proposed approach was applied to a problem of estimating wind directions that typically exhibit complex patterns with large variation and circularity.

scholarly journals Some New Robust Estimators for Circular Logistic Regression Model with Applications on Meteorological and Ecological Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shokrya S. Alshqaq ◽  
Abdullah A. Ahmadini ◽  
Ali H. Abuzaid
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Likelihood Estimation ◽  
Simulation Studies ◽  
Robust Estimators ◽  
Finite Sample ◽  
Ecological Data ◽  
Logistic Regression Models

Maximum likelihood estimation ( MLE ) is often used to estimate the parameters of the circular logistic regression model due to its efficiency under a parametric model. However, evidence has shown that the classical MLE extremely affects the parameter estimation in the presence of outliers. This article discusses the effect of outliers on circular logistic regression and extends four robust estimators, namely, Mallows, Schweppe, Bianco and Yohai estimator BY , and weighted BY estimators, to the circular logistic regression model. These estimators have been successfully used in linear logistic regression models for the same purpose. The four proposed robust estimators are compared with the classical MLE through simulation studies. They demonstrate satisfactory finite sample performance in the presence of misclassified errors and leverage points. Meteorological and ecological datasets are analyzed for illustration.

Sign in / Sign up

Export Citation Format

Share Document