scholarly journals Modeling Claim Frequency in Indonesia Auto Insurance Using Generalized Poisson-Lindley Linear Model

2020 ◽  
Vol 16 (3) ◽  
pp. 428
Author(s):  
Mardianto Karim ◽  
Aceng Komarudin Mutaqin
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Motor Vehicle ◽  
Estimation Method ◽  
Secondary Data ◽  
Likelihood Estimation ◽  
Linear Modeling ◽  
Auto Insurance ◽  
Generalized Poisson ◽  
Claim Frequency

This paper will discuss the modeling of claim frequency from Indonesian auto insurance using the generalized Poisson-Lindley linear model. This modeling method assumes that the data of claim frequency are from populations that follow generalized Poisson-Lindley distribution. Generalized Poisson-Lindley linear model is an alternative to modeling count data that contains overdispersion. The parameters in the generalized Poisson-Lindley linear model can be estimated using the maximum likelihood estimation method through Newton Raphson's iteration numerical method. The data are the secondary data took from XYZ Company for the 2013 policy which is overdispersed. The data contains policyholder partial loss claims for comprehensive motor vehicle insurance products. From the research conducted it was concluded that the data is suitable to be modeled with generalized Poisson-Lindley linear models and produce better models than ordinary Poisson linear modeling because of produced the smaller AIC value. Of the 3 predictor variables that are modeled on the frequency of claims, 2 variables influenced they are the use variable and vehicle brand variable.

scholarly journals Symmetry Analysis of the Uncertain Alternative Box-Cox Regression Model

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Liang Fang ◽  
Zaiying Zhou ◽  
Yiping Hong
Keyword(s):  
Numerical Simulations ◽  
Least Squares ◽  
Linear Models ◽  
Cox Regression ◽  
Cox Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
The Asymmetry ◽  
Box Cox Transformation ◽  
Theoretical Issues

The asymmetry of residuals about the origin is a severe issue in estimating a Box-Cox transformed model. In the framework of uncertainty theory, there are such theoretical issues regarding the least-squares estimation (LSE) and maximum likelihood estimation (MLE) of the linear models after the Box-Cox transformation on the response variables. Heretofore, only weighting methods for least-squares analysis have been available. This article proposes an uncertain alternative Box-Cox model to alleviate the asymmetry of residuals and avoid λ tending to negative infinity for uncertain LSE or uncertain MLE. Such symmetry of residuals about the origin is reasonable in applications of experts’ experimental data. The parameter estimation method was given via a theorem, and the performance of our model was supported via numerical simulations. According to the numerical simulations, our proposed ‘alternative Box-Cox model’ can overcome the problems of a grossly underestimated lambda and the asymmetry of residuals. The estimated residuals neither deviated from zero nor changed unevenly, in clear contrast to the LSE and MLE for the uncertain Box-Cox model downward biased residuals. Thus, though the LSE and MLE are not applicable on the uncertain Box-Cox model, they fit the uncertain alternative Box-Cox model. Compared with the uncertain Box-Cox model, the issue of a systematically underestimated λ is not likely to occur in our uncertain alternative Box-Cox model. Both the LSE and MLE can be used directly without constructing a weighted estimation method, offering better performance in the asymmetry of residuals.

scholarly journals PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH

2021 ◽  
Vol 10 (2) ◽  
pp. 259-268
Author(s):  
Wahyu Sabtika ◽  
Alan Prahutama ◽  
Hasbi Yasin
Keyword(s):  
Maternal Mortality ◽  
Poisson Regression ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Mean Value ◽  
Significant Variable ◽  
Spatial Factors ◽  
Generalized Poisson ◽  
Central Java ◽  
Generalized Poisson Regression

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

Frequency Domain Identification of Multivariable Model for Aero-Engine using an Improved Maximum Likelihood Method

2015 ◽  
Vol 32 (3) ◽  
Author(s):  
Nan Liu ◽  
Jinquan Huang ◽  
Feng Lu ◽  
Muxuan Pan
Keyword(s):  
Maximum Likelihood ◽  
Frequency Domain ◽  
Linear Models ◽  
Estimation Method ◽  
Likelihood Method ◽  
Linear Modeling ◽  
Component Level ◽  
Ml Estimation ◽  
Domain Identification ◽  
Aero Engine

AbstractFor the linear modeling problem of multivariable system of aero-engine, considering the coupling between parameters, a multivariable maximum likelihood (ML) estimation method is researched. An improved expectation-maximization (EM) algorithm integrated genetic algorithm (GA) is proposed and applied to the process of ML identification of frequency domain. The amplitude, harmonic and phase vectors of odd-odd multi-sine exciting signal are designed and optimized. With the application of the proposed method, multivariable linear models of aero-engine at different operation states in flight envelope are established from nonlinear component-level model. The precision is demonstrated through simulations comparing to nonlinear model.

scholarly journals Estimation and Testing for Functional Form in Pure Premium Regression Models

1986 ◽  
Vol 16 (S1) ◽  
pp. S31-S43 ◽  
Author(s):  
Scott E. Harrington
Keyword(s):  
Functional Form ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Auto Insurance ◽  
Regression Methods ◽  
Special Cases ◽  
Insurance Claims Data ◽  
Log Linear

AbstractEstimation of pure premiums for alternative rate classes using regression methods requires the choice of a functional form for the statistical model. Common choices include linear and log-linear models. This paper considers maximum likelihood estimation and testing for functional form using the power transformation suggested by Box and Cox. The linear and log-linear models are special cases of this transformation. Application of the procedure is illustrated using auto insurance claims data from the state of Massachusetts and from the United Kingdom. The predictive accuracy of the method compares favorably to that for the linear and log-linear models for both data sets.

ANALISA KESEHATAN KEUANGAN PERUSAHAAN UNTUK MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN DENGAN MENGGUNAKAN METODE ALTMAN (STUDI KASUS PADA PERUSAHAAN MANUFAKTUR SEKTOR KOMPONEN OTOMOTIF YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011 – 2015)

2019 ◽  
Vol 13 (2) ◽  
pp. 55
Author(s):  
Indar Khaerunnisa ◽  
Edy Cahyadi
Keyword(s):  
Motor Vehicle ◽  
Stock Exchange ◽  
Secondary Data ◽  
Z Score ◽  
Financial Ratio ◽  
Average Value ◽  
Automotive Components ◽  
Grey Area ◽  
Financial Ratio Analysis ◽  
Year 2013

The Indonesian government has set the motor vehicle industry as one of the priority industries of the national interest, economic growth, and increased productivity. In order for the survival of a company is maintained, then the management should be able to maintain or even more spur increased performance. Various analyzes were developed to predict the beginning of the bankruptcy of the company. One analysis is widely used today is the analysis of Altman Z-Score, which this analysis refers to the financial ratios of the company. The purpose of this study was to analyze the bankruptcy of the automotive components companies that go public in Indonesia Stock Exchange year period 2011–2015. This study used a sample of four companies from the automotive components sector. Source of data is done by using secondary data. The data is processed by the method of the Z-score formula Z = 1,2X1 + 1,4X2 + 3,3X3 + 0,6X4 + 0,999X5. With the description of Z < 1,8 the company categorized into unhealthy/will be bankrupt, the value Z 1,8 < 2,99 the company is considered to be in the uncertain/grey area and the value of Z > 2,99 then the company is in a very healthy. In general, the results of these studies indicate that the four automtive components companies namely PT Astra Otoparts year 2011 value of Z = 14,67 year 2012 value of Z = 10,88 year 2013 value of Z = 13,90 year 2014 value of Z = 10,54 year 2015 value of Z = 4,94, PT Gajah Tunggal year 2011 value of Z = 5,72 year 2012 value of Z = 4,75 year 2013 value of Z = 3,10 year 2014 value of Z = 2,79 year 2015 value of Z = 1,58 and the average value of 2011-2015 periode Z = 3,59, PT Goodyear Indonesia year 2011 value of Z = 2,07 year 2012 value of Z = 2,44 year 2013 value of Z = 2,57 year 2014 value of Z = 2,02 year 2015 value of Z = 2,76, PT Indomobil Sukses Internasional year 2011 value of Z = 6,19 year 2012 value of Z = 3,99 year 2013 value of Z = 3,17 year 2014 value of Z = 2,59 year 2015 value of Z = 1,74. The average value 2011-2015 period showed 3 companies are in very healthy state and 1 company is in the uncertain/grey area. Keywords: Financial Ratio Analysis, Analysis of bankruptcy, Altman Z-Score Analysis, Automotive Components Company, Go Public.

scholarly journals Analysis of a Linear Model for Non-Synchronous Vibrations Near Stall

2021 ◽  
Vol 6 (3) ◽  
pp. 26
Author(s):  
Christoph Brandstetter ◽  
Sina Stapelfeldt
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Structural Vibration ◽  
Experimental Investigations ◽  
Vibration Modes ◽  
Critical Problem ◽  
Safety Critical ◽  
Unstable Vibration ◽  
Aero Engines ◽  
Lock In

Non-synchronous vibrations arising near the stall boundary of compressors are a recurring and potentially safety-critical problem in modern aero-engines. Recent numerical and experimental investigations have shown that these vibrations are caused by the lock-in of circumferentially convected aerodynamic disturbances and structural vibration modes, and that it is possible to predict unstable vibration modes using coupled linear models. This paper aims to further investigate non-synchronous vibrations by casting a reduced model for NSV in the frequency domain and analysing stability for a range of parameters. It is shown how, and why, under certain conditions linear models are able to capture a phenomenon, which has traditionally been associated with aerodynamic non-linearities. The formulation clearly highlights the differences between convective non-synchronous vibrations and flutter and identifies the modifications necessary to make quantitative predictions.

Institutional Quality and Trade Flow: Empirical Evidence from Malaysia and Other OIC Member Countries in Africa

2021 ◽  
pp. 097508782098717
Author(s):  
Hammed Agboola Yusuf ◽  
Luqman Olanrewaju Afolabi ◽  
Waliu Olawale Shittu ◽  
Kafilah Lola Gold ◽  
Murtala Muhammad
Keyword(s):  
Economic Growth ◽  
Bilateral Trade ◽  
Trade Flow ◽  
Estimation Method ◽  
Institutional Quality ◽  
Likelihood Estimation ◽  
Political Stability ◽  
Bilateral Trade Flow ◽  
World Governance Indicators ◽  
The Impact

This article examines the impact of institutional quality on bilateral trade flow between Malaysia and selected 25 African Organisation of Islamic Cooperation (OIC) member countries. Four institutional qualities were selected from World Governance Indicators with other trade predictors from the period from 1985 to 2016. Using gravity model of trade and Poisson pseudo-maximum likelihood estimation method (PPML) technique, the results confirm that government effectiveness, regulatory quality and political stability have an adverse effect on bilateral trade flow among the OIC countries in Africa. On the other hand, these institutional quality variables were considered as a strength for Malaysian economic growth. Therefore, better institutional quality reforms are needed among OIC member countries in Africa in order to accelerate trade, economic growth and development in their region.

scholarly journals A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

METRON ◽  
2021 ◽  
Author(s):  
Carlo Cavicchia ◽  
Pasquale Sarnacchiaro
Keyword(s):  
Job Satisfaction ◽  
Factor Analysis ◽  
Secondary School Teachers ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Higher Order ◽  
Teacher Job Satisfaction ◽  
Public Secondary School ◽  
Order Factor ◽  
Maximum Likelihood Estimation Method

AbstractTeachers’ performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers’ accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Common Assessment Framework and Education questionnaire was administered to 163 Italian public secondary school teachers to collect data, and a second-order factor analysis was used to detect which factors impact on Teacher Job Satisfaction, and to what extent. This model-based approach guarantees to detect factors which respect important properties: unidimensionality and reliability. All the coefficients are estimated according to the maximum likelihood estimation method in order to make inference on the parameters and on the validity of the model. Moreover, a new multi-group test for higher-order factor analysis was proposed and implemented. Finally, we analyzed in detail whether the factors impacting Teacher Job Satisfaction are characterized by gender.

SiFi

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Deke Guo ◽  
Xiaoqiang Teng ◽  
Yulan Guo ◽  
Xiaolei Zhou ◽  
Zhong Liu
Keyword(s):  
Rapid Development ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Location Based Services ◽  
Shopping Mall ◽  
Inference Problem ◽  
Indoor Location ◽  
Label Semantics ◽  
Geometrical Constraints ◽  
Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

scholarly journals Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1231
Author(s):  
Yunbo Shi ◽  
Juanjuan Zhang ◽  
Jingjing Jiao ◽  
Rui Zhao ◽  
Huiliang Cao
Keyword(s):  
Wavelet Packet ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Parameter Extraction ◽  
Accelerometer Sensor ◽  
Oscillation Phase ◽  
Mems Accelerometer ◽  
Optimal Tree ◽  
Adaptive Decomposition ◽  
Entropy Criterion

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

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