Pub Date : 2023-12-04DOI: 10.14710/medstat.16.1.76-87
M. Mukid, B. W. Otok, Suparti Suparti
In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.
在结构方程建模（SEM）中，通常假定所有观测值只遵循一个模型。如果观察结果包含自然组，而每个自然组都有不同的 SEM 模型，那么这种假设就变得无关紧要了。Mukid 等人（2002 年）提出了偏最小二乘修正模糊聚类法（PLSMFC），以此来寻找观测数据组，同时估计 SEM 模型的参数。本研究旨在了解 PLSMFC 方法在寻找以不同形式的结构方程模型为特征的观察组方面的性能。为了实现这一目标，我们进行了一项涉及 SEM 模型规格和聚类数量等因素的模拟研究。使用的程序是将生成的数据强制分为不同数量的分段。使用的分段有效性度量是模糊性能指数（FPI）和归一化分类熵（NCE）。FPI 和 NCE 的最小值表示正确的分段数。根据模拟研究可知，PLSMFC 方法能够准确检测分段，尤其是当用于重新分配观测值的分段大小大于用于生成数据的分段数量时。
{"title":"SIMULATION STUDY FOR UNDERSTANDING THE PERFORMANCE OF PARTIAL LEAST SQUARES–MODIFIED FUZZY CLUSTERING (PLSMFC) IN FINDING GROUPS UNDER STRUCTURAL EQUATION MODEL","authors":"M. Mukid, B. W. Otok, Suparti Suparti","doi":"10.14710/medstat.16.1.76-87","DOIUrl":"https://doi.org/10.14710/medstat.16.1.76-87","url":null,"abstract":"In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"274 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139012397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.14710/medstat.16.1.88-99
E. Sunandi, K. Notodiputro, Indahwati Indahwati, A. Soleh
Small Area Estimation is a statistical method used to estimate parameters in sub-populations with small or even no sample sizes. This research aims to evaluate the Beta-Binomial model's performance for estimating small areas at the area level. The estimation method used is Hierarchical Likelihood (HL). The data used are simulation data and empirical data. Simulation studies were used to investigate the proposed model. The estimator's Mean Squared Error of Prediction (MSEP) and Absolute Bias (AB) estimator values determine the best estimation criteria. An empirical study using data on the illiteracy rate at the sub-district level in Bengkulu Province. The results of the simulation study show that, in general, the parameter estimators are nearly unbiased. Proportion prediction has the same tendency as parameters. Finally, the HL estimator has a small MSEP estimator. The results of an empirical study show that the average illiteracy rate in Bengkulu province is quite diverse. Kepahiang District has the highest average illiteracy rate in Bengkulu Province in 2021.
{"title":"BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH","authors":"E. Sunandi, K. Notodiputro, Indahwati Indahwati, A. Soleh","doi":"10.14710/medstat.16.1.88-99","DOIUrl":"https://doi.org/10.14710/medstat.16.1.88-99","url":null,"abstract":"Small Area Estimation is a statistical method used to estimate parameters in sub-populations with small or even no sample sizes. This research aims to evaluate the Beta-Binomial model's performance for estimating small areas at the area level. The estimation method used is Hierarchical Likelihood (HL). The data used are simulation data and empirical data. Simulation studies were used to investigate the proposed model. The estimator's Mean Squared Error of Prediction (MSEP) and Absolute Bias (AB) estimator values determine the best estimation criteria. An empirical study using data on the illiteracy rate at the sub-district level in Bengkulu Province. The results of the simulation study show that, in general, the parameter estimators are nearly unbiased. Proportion prediction has the same tendency as parameters. Finally, the HL estimator has a small MSEP estimator. The results of an empirical study show that the average illiteracy rate in Bengkulu province is quite diverse. Kepahiang District has the highest average illiteracy rate in Bengkulu Province in 2021.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"213 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.14710/medstat.16.1.67-75
Triastuti Wuryandari, Yuciana Wilandari
Survival analysis is a branch of statistics for analyzing the duration of time until one or more events occur. Time to recurrence of diabetics including survival data. Diabetes can’t be cured but it can be controlled. Diabetics who don’t maintain their health and lifestyle will experience recurrence. Factors thought to influence the recurrence of diabetics are internal factors such as genetics and external factors such as lifestyle. The recurrence time of an object includes recurrent events because each object can experience the same recurrent event during the follow-up. One of the analysis to determine factors that are thought to influence the recurrence time of diabetics is survival analysis. Survival data can be modeled into a regression model if the survival time of an object is influenced by other factors. One of the regression models for survival data is Cox regression. One of the Cox regression models for recurrent event data is the AG model which uses a counting process approach. This study used data on the recurrence of diabetics at MH Thamrin Cileungsi Hospital. Based on data analysis, factors that influence the recurrence of diabetics are age, gender, and type of complication.
{"title":"SURVIVAL ANALYSIS FOR RECURRENT EVENT DATA USING COUNTING PROCESS APPROACH: APPLICATION TO DIABETICS","authors":"Triastuti Wuryandari, Yuciana Wilandari","doi":"10.14710/medstat.16.1.67-75","DOIUrl":"https://doi.org/10.14710/medstat.16.1.67-75","url":null,"abstract":"Survival analysis is a branch of statistics for analyzing the duration of time until one or more events occur. Time to recurrence of diabetics including survival data. Diabetes can’t be cured but it can be controlled. Diabetics who don’t maintain their health and lifestyle will experience recurrence. Factors thought to influence the recurrence of diabetics are internal factors such as genetics and external factors such as lifestyle. The recurrence time of an object includes recurrent events because each object can experience the same recurrent event during the follow-up. One of the analysis to determine factors that are thought to influence the recurrence time of diabetics is survival analysis. Survival data can be modeled into a regression model if the survival time of an object is influenced by other factors. One of the regression models for survival data is Cox regression. One of the Cox regression models for recurrent event data is the AG model which uses a counting process approach. This study used data on the recurrence of diabetics at MH Thamrin Cileungsi Hospital. Based on data analysis, factors that influence the recurrence of diabetics are age, gender, and type of complication.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"48 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139203809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.14710/medstat.16.1.100-111
Luthfatul Amaliana, Andi Prasetya
This study aims to make a comparison related to the spatial weighted matrix of power and queen in the SEBLUP model to estimate per capita expenditure in East Java in 2019. The data used is secondary data then the data were analyzed by the Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). The results of this study indicate that the best spatial weighted matrix for estimating per capita expenditure in East Java using the SEBLUP model is the spatial weighted matrix of Queen, because it produces the smallest MSE value. In this study, the factors that significantly affect East Java's per capita expenditure are population density (X1), number of health facilities (X2), number of public elementary schools (X3), and the percentage of residents who have BPJS as the Fund Assistance Recipients (X5). The novelty of this study are combining multiple determinant factors that have demonstrated their substantial/significant effect on the average per capita expenditure and focusing on the regions characters in intermediate size (16
{"title":"COMPARISON OF SPATIAL WEIGHTED MATRIX BETWEEN POWER AND QUEEN ON THE SPATIAL EMPIRICAL BEST LINEAR UNBIASED PREDICTION MODEL (Study on Per Capita Expenditure in East Java Province in 2019)","authors":"Luthfatul Amaliana, Andi Prasetya","doi":"10.14710/medstat.16.1.100-111","DOIUrl":"https://doi.org/10.14710/medstat.16.1.100-111","url":null,"abstract":"This study aims to make a comparison related to the spatial weighted matrix of power and queen in the SEBLUP model to estimate per capita expenditure in East Java in 2019. The data used is secondary data then the data were analyzed by the Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). The results of this study indicate that the best spatial weighted matrix for estimating per capita expenditure in East Java using the SEBLUP model is the spatial weighted matrix of Queen, because it produces the smallest MSE value. In this study, the factors that significantly affect East Java's per capita expenditure are population density (X1), number of health facilities (X2), number of public elementary schools (X3), and the percentage of residents who have BPJS as the Fund Assistance Recipients (X5). The novelty of this study are combining multiple determinant factors that have demonstrated their substantial/significant effect on the average per capita expenditure and focusing on the regions characters in intermediate size (16","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139337264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.14710/medstat.16.1.47-58
Aliyah Husnun Azizah, Nurjannah Nurjannah, A. Fernandes, Rosita Hamdan
Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
{"title":"GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM","authors":"Aliyah Husnun Azizah, Nurjannah Nurjannah, A. Fernandes, Rosita Hamdan","doi":"10.14710/medstat.16.1.47-58","DOIUrl":"https://doi.org/10.14710/medstat.16.1.47-58","url":null,"abstract":"Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.14710/medstat.16.1.59-66
Arief Rachman Hakim, R. Santoso, H. Yasin, Masithoh Yessi Rochayani
Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.
{"title":"MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY","authors":"Arief Rachman Hakim, R. Santoso, H. Yasin, Masithoh Yessi Rochayani","doi":"10.14710/medstat.16.1.59-66","DOIUrl":"https://doi.org/10.14710/medstat.16.1.59-66","url":null,"abstract":"Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"71 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-24DOI: 10.14710/medstat.16.1.37-46
T. Widiharih, Agus Rusgiyono, S. Sudarno, Bagus Arya Saputra
Financial institutions use credit scoring analysis to predict the probability that a customer will default. In this paper, we determine the probability of default using nonparametric survival analysis that are Kaplan-Meier and Nelson-Aalen. The analysis is based on survival function curves, cumulative hazard function curves, mean survival time, and standard error of estimators. Based on the curves of survival function for both Kaplan Meier and Nelson Aalen estimators relatively the same. Based on the curves of cumulative hazard function, mean survival time, and standard error the Nelson-Aalen estimators are slightly higher than Kaplan-Meier.
金融机构使用信用评分分析来预测客户违约的概率。在本文中，我们使用 Kaplan-Meier 和 Nelson-Aalen 等非参数生存分析法来确定违约概率。该分析基于生存函数曲线、累积危害函数曲线、平均生存时间和估计值的标准误差。Kaplan Meier 和 Nelson Aalen 估计器的生存函数曲线相对相同。根据累积危害函数曲线、平均存活时间和标准误差，纳尔逊-阿伦估计值略高于卡普兰-迈尔估计值。
{"title":"KAPLAN-MEIER AND NELSON-AALEN ESTIMATORS FOR CREDIT SCORING","authors":"T. Widiharih, Agus Rusgiyono, S. Sudarno, Bagus Arya Saputra","doi":"10.14710/medstat.16.1.37-46","DOIUrl":"https://doi.org/10.14710/medstat.16.1.37-46","url":null,"abstract":"Financial institutions use credit scoring analysis to predict the probability that a customer will default. In this paper, we determine the probability of default using nonparametric survival analysis that are Kaplan-Meier and Nelson-Aalen. The analysis is based on survival function curves, cumulative hazard function curves, mean survival time, and standard error of estimators. Based on the curves of survival function for both Kaplan Meier and Nelson Aalen estimators relatively the same. Based on the curves of cumulative hazard function, mean survival time, and standard error the Nelson-Aalen estimators are slightly higher than Kaplan-Meier.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-10DOI: 10.14710/medstat.16.1.25-36
Hengki Muradi, A. Saefuddin, I. Sumertajaya, A. Soleh, Dede Dirgahayu Domiri
Support Vector Machines (SVMs) have received extensive attention over the last decade because it is claimed to be able to produce models that are accurate and have good predictions in various situations. This study aims to test the SVR (Support Vector Regression) method for modeling the growth phase of paddy using sentinel-1 image data. This method was compared for its accuracy with the LR (Linear Model) method using RMSE and R2 statistics and model stability using 10 repetitions. The accuracy of the model with the two best predictors is when the NDPI and API Polarization Index are the predictors. The paddy age model from the SVR method is better than the paddy age model from the LR method, where the SVR method produces a model with an average RMSE of 11.13 and an average coefficient of determination of 88.10%. The accuracy of the SVR model with NDPI and API predictors can be improved by adding VH polarization to the model, where the average RMSE statistic decreases to 11.0 and the average coefficient of determination becomes 88.42%. In this scenario, the best model gives a minimum RMSE value of 10.35 and a coefficient of determination of 90.05%.
{"title":"SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA","authors":"Hengki Muradi, A. Saefuddin, I. Sumertajaya, A. Soleh, Dede Dirgahayu Domiri","doi":"10.14710/medstat.16.1.25-36","DOIUrl":"https://doi.org/10.14710/medstat.16.1.25-36","url":null,"abstract":"Support Vector Machines (SVMs) have received extensive attention over the last decade because it is claimed to be able to produce models that are accurate and have good predictions in various situations. This study aims to test the SVR (Support Vector Regression) method for modeling the growth phase of paddy using sentinel-1 image data. This method was compared for its accuracy with the LR (Linear Model) method using RMSE and R2 statistics and model stability using 10 repetitions. The accuracy of the model with the two best predictors is when the NDPI and API Polarization Index are the predictors. The paddy age model from the SVR method is better than the paddy age model from the LR method, where the SVR method produces a model with an average RMSE of 11.13 and an average coefficient of determination of 88.10%. The accuracy of the SVR model with NDPI and API predictors can be improved by adding VH polarization to the model, where the average RMSE statistic decreases to 11.0 and the average coefficient of determination becomes 88.42%. In this scenario, the best model gives a minimum RMSE value of 10.35 and a coefficient of determination of 90.05%.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.14710/medstat.16.1.13-24
M. Miftahuddin, Ziqratul Husna, Eddy Gunawan, Syawaliah Muchtar
Farmer's Exchange Rate (FER) is one indicator to see the level of farmers' welfare. From 2014 to 2020, Aceh Province's FER was below 100 which indicates that farmers have not yet reached the level of welfare. This happens because of various factors including the price received by farmers (IR) is smaller than the price paid by farmers (IP). To find out the factors that influence the FER, it is necessary to do an analysis by forming a model. In this study, modeling of the FER data will be carried out, and see the factors that influence the index number with the longitudinal data regression approach. There are three estimation models, i.e. Common Effect Model, Fixed Effect Model, and Random Effect Model. Model selection of the best model is by using the Chow, Hausman, and Lagrange Multiplier tests. Furthermore, test the significance of the parameters using the simultaneous and partial tests and also see the value of the coefficient of determination (R2). The results obtained indicate that the appropriate model for the IR and IP data is the Random Effect Model where the R2for the IR and IP models are 67.06% and 85.42 respectively.
农民汇率（FER）是衡量农民福利水平的指标之一。从 2014 年到 2020 年，亚齐省的 FER 一直低于 100，这表明农民尚未达到福利水平。出现这种情况的原因有很多，包括农民获得的价格（IR）低于农民支付的价格（IP）。为了找出影响 FER 的因素，有必要通过建立模型来进行分析。本研究将对 FER 数据进行建模，通过纵向数据回归法了解影响指数的因素。有三种估计模型，即共同效应模型、固定效应模型和随机效应模型。通过周检验、豪斯曼检验和拉格朗日乘数检验来选择最佳模型。此外，还使用同时检验和部分检验来测试参数的显著性，并查看决定系数（R2）的值。结果表明，适合 IR 和 IP 数据的模型是随机效应模型，其中 IR 和 IP 模型的 R2 分别为 67.06% 和 85.42。
{"title":"MODELING OF FARMER EXCHANGE RATE IN ACEH PROVINCE USING LONGITUDINAL DATA ANALYSIS","authors":"M. Miftahuddin, Ziqratul Husna, Eddy Gunawan, Syawaliah Muchtar","doi":"10.14710/medstat.16.1.13-24","DOIUrl":"https://doi.org/10.14710/medstat.16.1.13-24","url":null,"abstract":"Farmer's Exchange Rate (FER) is one indicator to see the level of farmers' welfare. From 2014 to 2020, Aceh Province's FER was below 100 which indicates that farmers have not yet reached the level of welfare. This happens because of various factors including the price received by farmers (IR) is smaller than the price paid by farmers (IP). To find out the factors that influence the FER, it is necessary to do an analysis by forming a model. In this study, modeling of the FER data will be carried out, and see the factors that influence the index number with the longitudinal data regression approach. There are three estimation models, i.e. Common Effect Model, Fixed Effect Model, and Random Effect Model. Model selection of the best model is by using the Chow, Hausman, and Lagrange Multiplier tests. Furthermore, test the significance of the parameters using the simultaneous and partial tests and also see the value of the coefficient of determination (R2). The results obtained indicate that the appropriate model for the IR and IP data is the Random Effect Model where the R2for the IR and IP models are 67.06% and 85.42 respectively.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.14710/medstat.15.2.175-185
Gede Ary Prabha Yogesswara, D. Qoyyimi, Abdurakhman Abdurakhman
BPJS Kesehatan is a legal entity established to administer the health service program using the insurance system. Heart related diseases is a disease with the largest coverage cost in Indonesia. It can be calculated by using the collective risk model as an approximation of the aggregate loss model. This model is a compound distribution from claim frequency and claim severity, where claim frequency be the primary distributions. The Poisson distribution can be used to the distribution of the heart disease claim frequency. Whereas, the distribution of the heart disease claim severity has a lognormal distribution. The model obtained can explain the aggregate loss of heart disease claims properly.
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