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Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 4, No 2 (2011): Media Statistika" : 6 Documents clear
PENANGANAN OVERDISPERSI PADA MODEL REGRESI POISSON MENGGUNAKAN MODEL REGRESI BINOMIAL NEGATIF Simarmata, Rio Tongaril; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (413.945 KB) | DOI: 10.14710/medstat.4.2.95-104

Abstract

Poisson regression is the most popular tool for modeling the relationship between a discrete data in the response variable and a set of predictors with continue, discrete, categoric or mix data. Response variable with discrete data, however, may overdispersed or underdispersed, not conductive to Poisson regression which assumed that the mean value equals to variance  (equidispersed). One of the model that be used to overdispersed the discrete data is a regression model based on mixture distribution namely Poisson-gamma mixture which result negative binomial distribution. This regression model usually known as binomial negative regression. Using Generalized Linier Model (GLM) approach, the given model, parameter estimate, diagnostics, and interpretation of negative binomial regression can be determined.   Keyword: Negative Binomial Distribution, Dispersion, Generalized Linier Model
DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI Pramesti, Getut
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (234.82 KB) | DOI: 10.14710/medstat.4.2.105-112

Abstract

An Aggregation of claims are claims the sum of individual claims can be described in a distribution of collective risks that occur in a single period of insurance. Distribution is depicted in a probability density function and cumulative density function. These functions can also describe the characteristics of the distribution through the mean and variance. Writing this paper is to determine the aggregate claims model with a amount individual claims Rayleigh distributed and the number of claims Poisson distributed. Discussion of the results obtained showed that the model's claim depends on the aggregation of individual claims and the number of claims that occurred during the period of insurance.   Keywords: Aggregation, Claim, Rayleigh
PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION Yasin, Hasbi
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.595 KB) | DOI: 10.14710/medstat.4.2.63-72

Abstract

Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method. Keywords:   Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method
DISTRIBUSI POISSON DAN DISTRIBUSI EKSPONENSIAL DALAM PROSES STOKASTIK Sugito, Sugito; Mukid, Moch Abdul
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (239.898 KB) | DOI: 10.14710/medstat.4.2.113-120

Abstract

In the queueing system, the processes usually come from a Poisson process. In this system should be obtained an arrival distribution and a service distribution. This paper studies about the form of the number of arrival distribution, the number of service distribution, the interarrival distribution and the service time distribution. Futhermore it talks about the relation of them to a Poisson distribution and  an exponential distribution.   Keywords: Poisson Process, Poisson Distribution, Eksponential Distribution
PENDUGAAN DATA HILANG DENGAN MENGGUNAKAN DATA AUGMENTATION Nova, Mesra; Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.446 KB) | DOI: 10.14710/medstat.4.2.73-86

Abstract

Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing data if the observed data are known. The next step is posterior or P-step where the estimation process of parameter values ​​from the complete data is conducted. Imputation and posterior steps on the data augmentation will continue to run until the convergence is reached. The estimate of missing data is obtained through the average of simulated values.   Keywords: Missing Data, Data Augmentation, Imputation Step, Posterior Step
FUZZY PARAMETRIC SAMPLE SELECTION MODELS OF MARRIED WOMEN FOR NON-PARTICIPATION BY MLE : CASE STUDY THE MPFS-1994 Safiih, L. Muhamad; Triana, Yaya Sudarya
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.986 KB) | DOI: 10.14710/medstat.4.2.87-94

Abstract

Models with sample-selection biases are widely used in various fields of economics such as labour economics (see Maddala, Amemiya, and Mroz). The models are usually estimated by Heckman's two-step estimator. However, Heckman's two-step estimator often performs poorly (see Wales and Woodland, Nelson, Paarsch, and Nawata). The data used in this study originated from the survey was conducted by the National Population and Family Development Board of Malaysia under the Ministry of Women, Family and Community Development of Malaysia, called the Malaysian Population and Family Survey 1994 (MPFS, 1994).  The survey was conducted through a questionnaire, were randomly and specifically for married women. The data set focus on married women which provides information on wages, educational attainment, household composition and other socioeconomic characteristic. The Original sample data based on Mroz (1987), there are 4444 records married women. It is necessary to use the maximum likelihood method to estimate the models in such cases. For solving uncertainty data of  a parametric sample selection model, in this paper needs to consider the models estimation using fuzzy modeling approach, called Fuzzy Parametric Sample Selection Model (FPSSM). Fuzzy Parametric sample selection model (FPSSM) is builds as a hybrid to the  conventional parametric sample selection model. Finally, the result showed, FPSSM by Maximum Likelihood Estimator (MLE) estimates of the mean, Standard Deviation (SD).   Keywords:    Econometrics, Fuzzy Number, Heckman Two-Step Estimator,  Married Women,  MLE, Non-participation, Sample Selection Model.

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