Articles
<![CDATA[Pendugaan Regresi Spline Terpenalti dengan Pendekatan Model Linear Campuran ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Aunuddin Aunuddin]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v6i1.935
<![CDATA[Regresi spline terpenalti, atau P-spline, adalah regresi yang ditentukan dengan kuadrat terkecil danpenalti kekasaran. P-spline dapat direpresentasikan dalam bentuk model linear campuran dengankomponen ragam mengontrol tingkat ketidaklinearan dari penduga fungsi mulusnya. Pendugaan Psplinedengan pendekatan model linear campuran mempunyai tiga keuntungan. Keuntunganpertama adalah P-spline dapat diduga dengan metode kemungkinan maksimum (ML) atau denganmetode kemungkinan maksimum berkendala (REML). Keuntungan kedua adalah komputasi lebihcepat karena menggunakan basis pemulus berdimensi rendah. Keuntungan ketiga adalah P-splinedapat dikembangkan untuk model dengan peubah penjelas lebih dari satu.]]>
<![CDATA[Keunggulan Pendugaan Model Aditif dengan Pendekatan Model Linear Campuran Dibanding dengan Algoritma Backfitting ]]>
<![CDATA[Anik Djuraidah]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 8, No 1 (2008) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v8i1.977
<![CDATA[The additive model is the generalized of multiple linear regression that expresses the mean of areponse variable as a sum of functional form of predictors. The widely used estimation of additivemodels described in Hastie and Tibshirani (1990) is backfitting algorithm. However, the algorithmwith linear smoothers gave some difficulties when it comes to model selection and its inference. Theadditive model with P-spline as smooth function admits a mixed model formulation, in whichvariance components control the non-linearity degree in the smooth function. This research isfocused in comparing of estimation additive models using backfitting algorithm and linear mixedmodel approach. The research results show that estimation of additive models using linear mixedmodels offer simplicity in the computation, since use low-rank dimension of P-spline, and in themodel inference, since based on maximum likelihood method. Estimation additive model using linearmixed model approach can be suggested as an alternative method beside backfitting algorithm]]>
<![CDATA[Analisis Risiko Operasional Bank XXX dengan Metode Teori Nilai Ekstrim ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Pika Silvianti]]>;
<![CDATA[Aris Yaman]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 2 (2011) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v11i2.1054
<![CDATA[Bank in its operations are always exposed to risks that are closely related, because of its position as afinancial intermediary institutions. One of the risks which arise when this is operational risk.Operational risk to be one additional factor that must be measured and taken into account in theminimum capital adequacy, in addition to credit and market risk. There are three approaches forsetting capital charges for operational risk, are Basic Indicator Approach, Standardized Approach andAdvanced Measurement Approach. This research used the Advanced Measurement Approach inparticular the use of Extreme Value Theory (EVT) to measure the bank XXX operational risk, this isbecause the distribution of operational risk data have a tendency panhandle. Extreme valueidentification method used is the Peaks over Threshold (POT) method. The results showed that theamount of funds bank XXX must reserve to cover the possibility of operational risk in the period of2010 amounted to Rp 737,210,874, - at 99.9% confidence level. Backtesting results demonstrate thatviable models to be used as a means of measuring operational risk by 99.9% confidence level, for alltypes of operational risk events.]]>
<![CDATA[Regresi Spasial untuk Menentuan Faktorfaktor Kemiskinan di Provinsi Jawa Timur ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Aji Hamim Wigena]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 12, No 1 (2012) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v12i1.1055
<![CDATA[Dalam menentukan suatu wilayah kabupaten tergolong miskin umumnya masih digunakan analisisregresi. Padahal kemiskinan sangat mungkin terpengaruh oleh ruang dan daerah sekitarnya. Kondisiini menyebabkan data antar pengamatan sulit memenuhi asumsi saling bebas sebagai salah satuasumsi pada analisis regresi. Analisis yang dapat mengakomodir masalah spasial ini adalah modelotoregresif spasial (spatial autoregressive models, SAR), model galat spasial galat (spatial error models,SEM), dan model spasial umum (spatial general models, SGM). Tujuan penelitian ini adalahmenentukan faktor-faktor yang mempengaruhi kemiskinan dengan model regresi spasial. Hasilpenelitian menunjukkan bahawa model terbaik adalah SAR dan faktor-faktor yang mempengaruhikemiskinan adalah persentase penduduk yang tidak tamat Sekolah Dasar (SD) atau tidak bersekolah,persentase penduduk yang menggunakan air minum yang tidak berasal dari air mineral, air PAM,pompa air, sumur atau mata air terlindung, dan persentase penduduk yang menempati rumah dengankategori sehat yaitu dengan luas lantai lebih dari 8 m2.]]>
<![CDATA[Geo-additive Models in Small Area Estimation of Poverty ]]>
<![CDATA[Novi Hidayat Pusponegoro]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Anwar Fitrianto]]>;
<![CDATA[I Made Sumertajaya]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.15
<![CDATA[Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.]]>
<![CDATA[PENERAPAN METODE COKRIGING DENGAN VARIOGRAM ISOTROPI DAN ANISOTROPI DALAM MEMPREDIKSI CURAH HUJAN BULANAN JAWA BARAT ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Septian Rahardiantoro]]>;
<![CDATA[Azizah Desiwari]]>
<![CDATA[ Jurnal Meteorologi dan Geofisika Vol 20, No 1 (2019) ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengembangan BMKG ]]>
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DOI: 10.31172/jmg.v20i1.594
<![CDATA[Curah hujan merupakan salah satu unsur iklim yang penting dalam pertanian. Informasi mengenai ukuran curah hujan dapat diketahui dari pos hujan pada suatu wilayah. Permasalahan yang dihadapi adalah tidak semua wilayah memiliki pos hujan, sehingga metode interpolasi spasial dapat digunakan dalam memprediksi besarnya curah hujan pada suatu wilayah. Metode cokriging merupakan salah satu metode interpolasi spasial yang bersifat Best Linear Unbiased Prediction (BLUP) dengan melibatkan minimum dua peubah. Peubah yang digunakan dalam penelitian ini dipilih berdasarkan keeratan hubungannya, yaitu peubah curah hujan dan elevasi pos hujan. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan tahun 1981 hingga 2013 pada 38 pos hujan di wilayah Jawa Barat. Metode analisis diawali dengan menetukan variogram isotropi yang ditentukan berdasarkan jarak spasial dan variogram anisotropi yang ditentukan berdasarkan jarak dan arah pada kedua peubah. Selanjutnya, variogram yang terbaik digunakan untuk prediksi curah hujan. Hasil penelitian menunjukkan variogram terbaik adalah variogram isotropi dengan hasil prediksi curah hujan bulanan yang mempunyai nilai reduced means square error berkisar antara 0.54 sampai dengan 1.46 dan nilai average error hampir 0.Rainfall is one of the important climatic elements in agriculture. The information on the amount of rainfall can be known from the weather station in a region. The problem faced is not all regions have its own weather station, so that spatial interpolation can be used to predict the amount of rainfall in a region. Cokriging is one of spatial interpolation that has properties BLUP (Best Linear Unbiased Prediction) that involved at least two variables. In this study, the variables used were the amount of rainfall and elevation of the weather station because these variables have a correlation. The data used in this study were monthly rainfall from 1981 to 2013 at 38 weather stations in West Java. The first step in analysis data was determined isotropy variogram determined based on spatial distance and anisotropic variogram determined based on distance and direction in the two variables. Furthermore, the best variogram was used for the rainfall prediction. The results showed the best variogram is isotropy with the results of monthly rainfall predictions with the cokriging method having reduced means square error values ranging from 0.54 to 1.46 and the average error value of almost 0. ]]>
<![CDATA[The Application of Modeling Gamma-Pareto Distributed Data Using GLM Gamma in Estimation of Monthly Rainfall with TRMM Data ]]>
<![CDATA[Herlina Hanum]]>;
<![CDATA[Aji Hamim Wigena]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[I Wayan Mangku]]>
<![CDATA[ Sriwijaya Journal of Environment Vol 2, No 2 (2017): Water As A Vital Resource for Life ]]>
Publisher : <![CDATA[Program Pascasarjana Universitas Sriwijaya ]]>
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DOI: 10.22135/sje.2017.2.2.40-45
<![CDATA[As a recently developed distribution, the application of Gamma-Pareto is limited to single variable modeling. A specific transformation of Gamma-Pareto (G-P) yields gamma distribution. Therefore, it is possible to use analysis based on gamma distribution (e.g. GLM) for modeling G-P distributed data. In this paper we study the application of modeling G-P distributed data using GLM gamma for monthly rainfall which observed in Sukadana Station. The modeling aims to analyze whether Tropical Rainfall Measuring Mission (TRMM) satellite data is a good estimator for unobserved station’s data. The transformed of station’s data were considered as response variable in GLM gamma. The explanatory variable is TRMM data in 9 grids around the station. There are two kinds of modeling i.e. model for whole data and extreme data. The results show that for both data the station’s data are G-P distributed and the transformed data are gamma distributed. TRMM rainfall data at each grid around the station can be used to estimate the observed data of monthly rainfall. The best model for both data contains dummy variables which correspond to inter quantile data. The coefficients of dummy variables in the best model may substitute the grouping or the correction in the previous studies.]]>
<![CDATA[PERAMALAN CURAH HUJAN EKSTRIM DI PROVINSI BANTEN DENGAN MODEL EKSTRIM SPASIAL ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Cici Suheni]]>;
<![CDATA[Banan Nabila]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.1.50-62
<![CDATA[Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.]]>
<![CDATA[PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA) ]]>
<![CDATA[Retsi Firda Maulina]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Anang Kurnia]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.140-151
<![CDATA[Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.]]>
<![CDATA[Handling Outliers in The Stochastic Frontier Model Using Cauchy and Rayleigh Distributions to Measure Technical Efficiency of Rice Farming Bussiness ]]>
<![CDATA[Retna Nurwulan]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Anwar Fitrianto]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v5i2.19597
<![CDATA[Technical Efficiency (TE) is one of the essential indicators used to evaluate the development of the agricultural sector. Generally, the statistical model used to measure TE is a stochastic frontier model with the noise being normally distributed and the inefficiency being half-normally distributed. The problem is that the model is not robust when outlier observations occur. This study proposed a stochastic production frontier model with a fat-tailed distribution to overcome outlier observations. This study used two stochastic models with fat-tailed distribution used in this study: Chaucy-half normal and normal-Rayleigh stochastic models. The translog production function was selected as a connecting function between the input and output. These two models were applied to estimate the technical efficiency of rice farming in Central Kalimantan. The results showed that the proposed model could reduce or eliminate outliers in the remaining inefficiencies. In addition, the range of technical efficiency values had also narrowed. Thus, the Chaucy-half normal and normal-Rayleigh stochastic models can handle outliers.]]>
