All Research Papers Published In Advances and Applications in Statistics

Research Article
10.17654/as071010001

ESTIMATION OF THE RELIABILITY FUNCTION OF THE INVERSE GAUSSIAN DISTRIBUTION UNDER A BAYESIAN BY USING THE CONCEPT OF A PREDICTIVE DISTRIBUTION

Nov 10, 2021
Advances and Applications in Statistics
Fuad S Al-Duais

Research Article
1
10.17654/as070020229

REGRESSION MODEL OF ELECTRICITY GENERATION AT NUCLEAR POWER PLANTS IN RUSSIA WITH RESPECT TO THE NONLINEAR PREDICTORS

Oct 20, 2021
Advances and Applications in Statistics
S I Noskov + 1 more +1

Research Article
1
10.17654/as070010031

VALUE AT RISK WITH STOCHASTIC VOLATILITY PERTURBED BY HURST PARAMETER

Jul 20, 2021
Advances and Applications in Statistics
Mohammed Alhagyan + 5 more +5

The commonly adopted value at risk (VaR) model assumes the distribution of portfolio values that are normally distributed with stationary and independent increment, which restricts its usage for cases of arbitrary distributions. In this study, the current assumption is improved by constructing theoretical development of VaR model based on fractional Brownian motion, with Hurst parameter, H, as its underlying distribution. Empirical investigation is conducted to compare the standard model with the proposed model. The findings indicated that higher value of H provided higher value in VaR, thus suggesting greater probability to lose.

Research Article
10.17654/as068010057

FORECASTING THE IMPORT VALUE OF THAILAND

Apr 10, 2021
Advances and Applications in Statistics
Sukanya Intarapak + 1 more +1

Research Article
1
10.17654/as068010071

USING OBSERVATION DRIVEN MODELS TO FIT COUNT DAILY NEW CONFIRMED CASES OF “COVID 19” IN EGYPT

Apr 10, 2021
Advances and Applications in Statistics
Hanan Elsaied

On 11 of March, the World Health Organization declared corona virus as a pandemic. This virus has spread in at least 218 countries with at least one case reported until the end of 2020. In Egypt, the first case was reported on 14/02/2020. At the end of 2020, it confirms its higher daily rise of new This paper suggests to use the autoregressive conditional Poisson (ACP) models, also known as integer-valued GARCH (briefly: INGARCH) models which belong to the observation driven models (ODMs) category to model the count daily new confirmed cases of COVID 19 in Egypt. We compare between the robust and the non-robust estimators for the Poisson INARCH(1) model as a simple class of the INGARCH (p, q) models by simulations. The application results indicate that the robust version of the Poisson INARCH(1) model is more reasonable to fit the number of new confirmed cases of COVID 19.

Research Article
10.17654/as067020179

MEASURE OF SLOPE ROTATABILITY FOR SECOND ORDER RESPONSE SURFACE DESIGNS UNDER INTRA-CLASS CORRELATED STRUCTURE OF ERRORS USING BALANCED INCOMPLETE BLOCK DESIGNS

Mar 20, 2021
Advances and Applications in Statistics
Sulochana Beeraka + 1 more +1

Research Article
7
10.17654/as066020175

JOINT RETURN PERIOD ESTIMATION OF DAILY MAXIMUM AND MINIMUM TEMPERATURES USING COPULA METHOD

Jan 20, 2021
Advances and Applications in Statistics
M A M Abraj + 1 more +1

Research Article
10.17654/as066010077

MODELING COUNT DATA USING POISSON REGRESSION IN EVALUATION OF EDUCATIONAL PERFORMANCE: A NEW PARADIGM

Jan 05, 2021
Advances and Applications in Statistics
Nawal G Alghamdi + 2 more +2

Research Article
10.17654/as066010061

ENSEMBLE CHURN RISK ANALYTICS

Jan 05, 2021
Advances and Applications in Statistics
Federico Bonelli + 2 more +2

Research Article
1
10.17654/as065010089

FORECASTING MEXICAN PESO - US DOLLAR EXCHANGE RATE AFTER THE 2020 PANDEMIC WITH GARCH AND XGBOOST TIME SERIES MODELS

Nov 20, 2020
Advances and Applications in Statistics
Alejandro Ruiz-Olivares + 2 more +2