scholarly journals POISSON REGRESSION OF DAMAGE PRODUCT SALES USING MCMC

2018 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Reny Rian Marliana ◽  
Septiadi Padmadisastra
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Model ◽  
Poisson Regression ◽  
Explanatory Variable ◽  
Actual Number ◽  
Input Process ◽  
Binomial Distributions ◽  
Gibbs Sampling Algorithm ◽  
Product Sales

In this paper a model for the number of “damage” product sales is studied. The product sales are run into underreporting counts, caused by a delay on input process of the system called sales cycle. The goal of the study is to estimate the parameters of the regression model of product sales on an explanatory variable. It is the actual number of product sales. The model used is a mixture of the Poisson and the Binomial distributions. The parameters of the regression model are estimated by a Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. The results of estimation clearly showed a gap between undamage product sales and the actual number. The gap is the number of damaged product sales.

scholarly journals Meningkatkan Ketahanan Wilayah Melalui Estimasi Underreported Data Kejahatan Menggunakan Pendekatan Bayes

2017 ◽  
Vol 23 (3) ◽  
pp. 376
Author(s):  
Herlin Venny Johannes ◽  
Septiadi Padmadisastra ◽  
Bertho Tantular
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gibbs Sampling ◽  
Actual Number ◽  
Sample Mean ◽  
Binomial Distributions ◽  
Estimate Parameter ◽  
Gibbs Sampling Algorithm ◽  
Ergodic Mean

ABSTRACTThis paper present a study for the number of crime that run into underreporting counts. The purpose of the analysis is to estimate parameter of the model which is the actual number of crime. The model is a mixture of the poisson and the binomial distributions developed by Winkelmann (1996). The parameters of the model are estimated by Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. Determination the convergence of the algorithm using trace plot, autocorrelation plot and ergodic mean plot. In the end, estimator of the parameters of the underreported counts model are the simulation sample mean that calculated from the simulation sample of iteration after burn in period until the last iteration.ABSTRAKPenelitian ini mengkaji permodelan data tingkat kejahatan yang mengalami underreporting counts. Tujuan analisis ini adalah untuk menaksir parameter model yaitu banyaknya jumlah tindak kejahatan yang sebenarnya.  Model yang digunakan adalah hasil penggabungan antara distribusi poisson dan distribusi binomial yang dikembengkan oleh Winkelmann (1996). Penaksiran parameter model dilakukan melalui pendekatan bayes dan simulasi Markov Chain Monte Carlo menggunakan algoritma gibbs sampling. Penentuan konvergensi algoritma akan dilakukan melalui trace plot, autocorrelation plot, dan ergodic mean plot. Taksiran parameter model diperoleh dari rata-rata nilai sampel hasil simulasi yang dihitung dari iterasi setelah burn in period sampai dengan iterasi yang terakhir.

scholarly journals Robustness Test of Selected Estimators of Linear Regression with Autocorrelated Error Term: A Monte-Carlo Simulation Study

2021 ◽  
pp. 1-17
Author(s):  
Rauf Ibrahim Rauf ◽  
Okoli Juliana Ifeyinwa ◽  
Haruna Umar Yahaya
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Linear Regression ◽  
Simulation Study ◽  
Explanatory Variable ◽  
Real Life ◽  
Least Square ◽  
Monte Carlo Simulation Study ◽  
Error Terms

Assumptions in the classical linear regression model include that of lack of autocorrelation of the error terms and the zero covariance between the explanatory variable and the error terms. This study is channeled towards the estimation of the parameters of the linear models for both time series and cross-sectional data when the above two assumptions are violated. The study used the Monte-Carlo simulation method to investigate the performance of six estimators: ordinary least square (OLS), Prais-Winsten (PW), Cochrane-Orcutt (CC), Maximum Likelihood (MLE), Restricted Maximum- Likelihood (RMLE) and the Weighted Least Square (WLS) in estimating the parameters of a single linear model in which the explanatory variable is also correlated with the autoregressive error terms. Using the models’ finite properties(mean square error) to measure the estimators’ performance, the results shows that OLS should be preferred when autocorrelation level is relatively mild (ρ = 0.3) and the PW, CC, RMLE, and MLE estimator will perform better with the presence of any level of AR (1) disturbance between 0.4 to 0.8 level, while WLS shows better performance at 0.9 level of autocorrelation and above. The study thus recommended the application of the various estimators considered to real-life data to affirm the results of this simulation study.

scholarly journals Assembly Deformation and Stresses of Compliant Structures

2011 ◽  
Vol 275 ◽  
pp. 69-72 ◽  
Author(s):  
Chen Song Dong ◽  
Lu Kang
Keyword(s):  
Finite Element Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Response Surface Methodology ◽  
Regression Model ◽  
Tolerance Analysis ◽  
Assembly Process ◽  
Element Analysis ◽  
Assembly Tolerance ◽  
Large Sheet

Compliant components such as large sheet metal components are commonly used in various products including automotive, aircraft and home appliances. Because of part-to-part variations, deformation and stresses are induced in the assembly process. An approach to the assembly tolerance analysis of compliant structures is presented in this paper. Given component deformation, assembly deformation and stresses are derived by finite element analysis (FEA). The influence of component deformation on assembly deformation and stresses is studied by response surface methodology (RSM), and a regression model is developed. Using the developed regression model, Monte Carlo simulation was conducted to study assembly tolerance and stresses. This approach is illustrated by an example.

scholarly journals Feasible Generalized Stein-Rule Restricted Ridge Regression Estimators

2017 ◽  
Vol 13 (1) ◽  
pp. 77-97
Author(s):  
Nimet Özbay ◽  
Issam Dawoud ◽  
Selahattin Kaçıranlar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Ridge Regression ◽  
Simulation Experiment ◽  
Regression Estimators ◽  
Coefficient Vector ◽  
General Linear Regression Model

Abstract Several versions of the Stein-rule estimators of the coefficient vector in a linear regression model are proposed in the literature. In the present paper, we propose new feasible generalized Stein-rule restricted ridge regression estimators to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model, when certain additional exact restrictions are placed on these coefficients. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others.

scholarly journals PENAKSIRAN PENJUALAN PRODUK BERDASARKAN PENDEKATAN MODEL REGRESI NEGATIF BINOMIAL

2019 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Reny Rian Marliana
Keyword(s):  
Monte Carlo ◽  
Regression Model ◽  
Negative Binomial ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Generalized Poisson ◽  
Regression Parameters ◽  
Binomial Regression ◽  
Product Sales

AbstrakPenelitian bertujuan untuk membandingkan hasil penaksiran parameter model regresi binomial negative dengan model Poisson untuk underreported counts pada penelitian sebelumnya. Model regresi dibentuk pada data penjualan produk yang mengalami underreporting counts, akibat keterlambatan input data ke aplikasi penjualan produk (sales cycle). Pada penelitian sebelumnya, model yang digunakan merupakan gabungan antara distribusi Binomial dan distribusi Poisson. Parameter model regresi ditaksir menggunakan pendekatan Bayes dan simulasi Markov Chain Monte Carlo melalui Algoritma Gibbs Sampling. Hasil penaksiran menunjukkan adanya perbedaan antara banyaknya penjualan yang dilaporkan dengan banyaknya penjualan produk yang sebenarnya. Besar perbedaan tersebut merupakan banyaknya penjualan produk yang tidak terlaporkan. Pada penelitian lanjutan ini, model yang digunakan adalah Model Regresi Negatif Binomial. Parameter regresi ditaksir menggunakan metode Iterasi Newton Rapson. Hasil penaksiran menunjukkan selisih yang cukup besar dimana model Poisson untuk underreported counts lebih robust sesuai dengan komponen musiman yang ada.Kata Kunci: underreported, generalized poisson, negative binomial  AbstractThe goal of study is to compare the parameters of the negative Binomial regression model and the Poisson Model for underreported counts in the previous study. A model is a regression model for the number of product sales that run ito underreporting counts, caused by a delay on input process to the product sales applications (called sales cycle). The model used in the previous study is a mixture of the poisson and the binomial distributions developed by Winkelmann (1996). The regression parameters are estimated by a Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. The results show the difference between the actual number and the reported number. This difference is the number of unreported product sales. In this study, the model used is the negative binomial regression model. The regression parameters are estimated using Newton Rapson iteration method. The results show a big gap from the previous study. It means that the Poisson Model for underreported counts is more robust in accordance with the seasonal components.Keywords: underreported, generalized poisson, negative binomial

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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