Robiah Adnan

Universiti Teknologi Malaysia

Normal distributionStatisticsBayesian probabilityEngineeringMean squared errorEconometricsRegression analysisGibbs samplingNonlinear systemLeast trimmed squaresVariablesCount dataMathematicsLinear regressionCategorical variableFuzzy logicRobust regressionPoisson regressionOutlierTruncated regression model

59Publications

9H-index

332Citations

Publications 59

Newest

#1Thanoon Y. ThanoonH-Index: 2

Last. Robiah AdnanH-Index: 9

view all 3 authors...

Bayesian Nonlinear Latent variable Models with Mixed Non-normal Variables and Covariates for Multi-sample Psychological Data

#1Thanoon Y. ThanoonH-Index: 2

#2Athar Talal Hamed (University of Mosul)

Last. Robiah Adnan (UTM: Universiti Teknologi Malaysia)H-Index: 9

view all 3 authors...

The purpose of this paper is to develop a latent variable model with nonlinear covariates and latent variables. Mixed ordered categorical and dichotomous variables and covariates with two different types of thresholds (with equal and unequal spaces) are used in Bayesian multi-sample nonlinear latent variable models and the Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution) and (truncated normal distribution w...

Improve the Bayesian generalized latent variable models with non-linear variable and covariate of dichotomous data

#1Thanoon Y. ThanoonH-Index: 2

#2Robiah AdnanH-Index: 9

#1Seyed Ehsan Saffari (NUS: National University of Singapore)H-Index: 11

#2Robiah Adnan (UTM: Universiti Teknologi Malaysia)H-Index: 9

Last. William H. Greene (NYU: New York University)H-Index: 83

view all 8 authors...

AbstractCount data with a single mode at zero and a few extreme counts in the outcome variable are frequently seen in medical research. In this article, the response variable—the number of doctor v...

Analysis of Generalized Nonlinear Structural Equation Models by Using Bayesian Approach with Application

#1Thanoon Y. Thanoon (UTM: Universiti Teknologi Malaysia)H-Index: 2

#2Robiah Adnan (UTM: Universiti Teknologi Malaysia)H-Index: 9

In this paper, Bayesian analysis is used in nonlinear structural equation models with two population of data and the Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of ordered categorical data in Bayesian multiple group SEMs and compared with the method that treats ordered categorical variables as a continuous normal distribution. Statistical inferences, which involve the estim...

Model comparison of Bayesian structural equation models with mixed ordered categorical and dichotomous data

#1Thanoon Y. ThanoonH-Index: 2

#2Robiah AdnanH-Index: 9

Last. Muhamad Alias Md. JediH-Index: 2

view all 3 authors...

The purpose of this paper is to describe the mixed variables (ordered categorical and dichotomous) in Bayesian structural equation models. Markov chain Monte Carlo simulation (MCMC) via Gibbs sampling method is applied for estimation the parameters. Statistical analyses, which include parameters estimation, standard error, higest posterior density and Devience information creterion for testing the prposed models, are discussed. Hidden continuous normal distribution with censoring is used to hand...

#1Thanoon Y. Thanoon (UTM: Universiti Teknologi Malaysia)H-Index: 2

#2Robiah Adnan (UTM: Universiti Teknologi Malaysia)H-Index: 9

In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedur...

Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers

#1Kafi Dano PatiH-Index: 2

#2Robiah AdnanH-Index: 9

Last. M D J Muhammad Alias (UTM: Universiti Teknologi Malaysia)H-Index: 1

view all 4 authors...

This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Huber ridge regression (HRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estima...

#1Thanoon Y. Thanoon (UTM: Universiti Teknologi Malaysia)H-Index: 2

#2Robiah AdnanH-Index: 9

In this paper, ordered categorical variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical analyses, which involve estimation of parameters and their standard deviations for testing the selected model, are discussed. The proposed procedure is illustrated by a simulation data obtained from R program. Data results are obtained from WinBUGS program.

Robust PC with wild bootstrap estimation of linear model in the presence of outliers, multicollinearity and heteroscedasticity error variance

#1Bello Abdulkadiri Rasheed (UTM: Universiti Teknologi Malaysia)H-Index: 1

#2Robiah AdnanH-Index: 9

Last. Seyed Ehsan SaffariH-Index: 11

view all 3 authors...

The regression model estimator is considered efficient if it is robust and resistant to the presence of heteroscedasticity variance, multicollinearity or unusual observations called outliers. However, in regard to these problems, the wild bootstrap and robust wild bootstrap are no longer efficient since they could not produce the smallest variance. Hence this research investigates the use of robust PC with wild bootstrap techniques on regression model as an estimator for real and simulation data...

Close Researchers

This website uses cookies.

We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.

To learn more, you can find in our Privacy Policy.

To learn more, you can find in our Privacy Policy.