A
PROFILE
UPM's Staff Profile
PROF. MADYA DR. JAYANTHI A/P ARASAN
PROFESOR MADYA
FACULTY OF SCIENCE
jayanthi
BM
EN

PROFESSIONAL

NAME :
(Prof. Madya Dr.) Jayanthi A/p Arasan
POSITION :
Profesor Madya
ENTITY :
Faculty Of Science

PUBLICATION
2021:
Detection of outliers in high-dimensional data using nu-support vector regression
2021:
Assessing the adequacy of the gompertz regression model in the presence of right censored data
2021:
Comparison of Drying Performance between Large and Medium-Sized Mobile Dryers for In-field Drying of Grain Corn
2021:
Modeling lifetime of parallel components sy covariate, right and interval censored data
2021:
Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis
2021:
SIMPLE AND FAST GENERALIZED - M (GM) ESTIMATOR AND ITS APPLICATION TO AIRCRAFT DATA SET
2021:
An Efficient Estimation and Classification Methods for High Dimensional Data Using Robust Iteratively Reweighted SIMPLS Algorithm Based on nu-Support Vector Regression
2021:
Inference for the Generalized Exponential Distributions With Covariate and Right-Censored Data
2021:
Mathematical Epidemiologic and Simulation Modelling of first wave COVID-19 in Malaysia
2021:
Go loud or go home? How power distance belief influences the effect of brand prominence on status consumption
2021:
Fast Improvised Influential Distance for the Identification of Influential Observations in Multiple Linear Regression
2020:
Influential measures on log-normal model for left-truncated and case-k interval censored data with time-dependent covariate
2020:
Effect of miscentering and low-dose protocols on contrast resolution in computed tomography head examination
2020:
Enhanced decolourization of methyl orange by immobilized TiO2/chitosan-montmorillonite
2020:
Assessing the Goodness of Fit of the Gompertz Model in the Presence of Right and Interval Censored Data with Covariate
2020:
Modified Cox-Snell Residuals in Evaluating Gompertz Regression Model with Censored Data
2020:
Automated Fitting Process Using Robust Reliable Weighted Average on Near Infrared Spectral Data Analysis
2020:
Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data†