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INDONESIA
Journal of the Indonesian Mathematical Society
Published by Indonesian Mathematical Society
ISSN : 20868952     EISSN : 24600245     DOI : -
Core Subject : Education,
Mathematics
Journal of the Indonesian Mathematical Society disseminates new research results in all areas of mathematics and their applications. Besides research articles, the journal also receives survey papers that stimulate research in mathematics and their applications.
Arjuna Subject : -
Articles 12 Documents
 1   2 
Search results for , issue "Volume 24 Number 1 (April 2018)" : 12 Documents clear
Star Projective and Star Injective Hv-Modules A. Mortazavi; Bijan Davvaz
Journal of the Indonesian Mathematical Society Volume 24 Number 1 (April 2018)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.24.1.490.79-94

Abstract

In this paper, we introduce the concepts of product and directsum, star projective and star injective in $H_v$-modules. Weinvestigate generalizations of some notions in homological algebrato prove the five lemma and star projective and star injectivetheorems in $H_v$-modules. We determine  equivalent conditions for split sequences in $H_v$-modules and  present some related results.
Bayesian Estimation of Random Parameter Models of Responses with Normal and Skew-t Distibutions Evidence from Monte Carlo Simulation Mohammad Masjkur; Henk Folmer
Journal of the Indonesian Mathematical Society Volume 24 Number 1 (April 2018)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.24.1.516.27-50

Abstract

Random parameter models have been found to outperform xed pa-rameter models to estimate dose-response relationships with independent errors. Amajor restriction, however, is that the responses are assumed to be normally andsymmetrically distributed. The purpose of this paper is to analyze Bayesian infer-ence of random parameter response models in the case of independent responseswith normal and skewed, heavy-tailed distributions by way of Monte Carlo simu-lation. Three types of Bayesian estimators are considered: one applying a normal,symmetrical prior distribution, a second applying a Skew-normal prior and, a thirdapplying a Skew-t-distribution. We use the relative bias (RelBias) and Root MeanSquared Error (RMSE) as valuation criteria. We consider the commonly applied lin-ear Quadratic and the nonlinear Spillman-Mitscherlich dose-response models. Onesimulation examines the performance of the estimators in the case of independent,normally and symmetrically distributed responses; the other in the case of indepen-dent responses following a heavy-tailed, Skew-t-distribution. The main nding isthat the estimator based on the Skew-t prior outperforms the alternative estima-tors applying the normal and Skew-normal prior for skewed, heavy-tailed data. Fornormal data, the Skew-t prior performs approximately equally well as the Skew-normal and the normal prior. Furthermore, it is more ecient than its alternatives.Overall, the Skew-t prior seems to be preferable to the normal and Skew-normal fordose-response modeling.

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