Mengatasi Error Berkorelasi Menggunakan Metode Transformasi Prewhitening pada Regresi Nonparametrik Kernel Bivariat

Suppose that given data {(1, 2, )} with nonparametric regression model :=1  = (1, 2) + ; = 1,2, ⋯ , with () is a regression function and is a random errors. In nonparametric regression often found correlated errors, i.e. the error value does not meet the identical and independent assumptions. Correlated errors will adversely affect the estimation model. Correlated errors can be resolved by prewhitening transformation method, a method where the error is assumed to follow the model ARMA (, ). Applied on data is shown that regression model was obtained with correlated errors. The error obtained from the conventional Kernel regression model follows the AR(1) model with the value ∅1= 0.932. After the prewhitening transformation, the kernel regression model results from the prewhitening transformation with uncorrelated errors. The MSE value of the conventional Kernel estimation modal is 639203.308 smaller than the MSE value of the estimated Kernel prewhitening transformation model that is 290303.832, so the Kernel estimator resulting from prewhitening transformation is more efficient than conventional Kernel estimator

Penerapan Model Vector Autoregressive Integrate Moving Average dalam Peramalan Laju Inflasi dan Suku Bunga di Indonesia

The inflation and interest rates in Indonesia have a significant impact on the country's economic development. Indonesian inflation and interest rates data are multivariate time series data that show activity over a certain period of time. Vector Autoregressive Integrated Moving Average (VARIMA) is a method for analyzing multivariate time series data. This method is a simultaneous equation modeling that has several endogenous variables simultaneously. This study aimed to model the inflation and interest rates data, from January 2009 to December 2016 and predict inflation and interest rates by using VARIMA method. The model obtained was the VARIMA(0,2,2) model, with estimated parameters using the maximum likelihood method. The choice of the VARIMA(0,2,2) model was based on the smallest AIC value of -4,2891, with a MAPE value for the inflation and interest rates forecasting were 6,04% and 1,84%, respectively, which indicates a very good forecast results

Klasifikasi Status Penerima Bantuan Program Keluarga Harapan Menggunakan Metode Analisis Diskriminan

Abstract The Problem that often occurs in the distribution of PKH is that the assistance provided is not targeted correctly. Efforts that can be made to solve these problems are by ensuring that the criteria for receiving PKH are accurate and in accordance with the applicable criteria. Based on these criteria, there needs to be a classification of household status that receives PKH assistanceand those who do not. This classification process can be done using Discriminant Analysis. The result of classification using discriminant analysis for the case of PKH assistance recipients in NTB Province obtained an APER value of 0.2450, which means a classification error rate of 25.4% or the classification result is considered accurate Keywords: Discriminant Analysis; APER; Classification; PKH.Abstrak Permasalahan yang sering terjadi dalam penbagian bantuan PKH adalah bantuan yang diberikan tidak tepat sasaran Upaya yang dapat dilakukan untuk menyelesaiakan permasalahan tersebut adalah dengan memastikan kriteria penerima bantuan PKH sudah tepat dan sesuai dengan ketentuan kriteria yang berlaku. Berdasarkan kriteria-kriteria tersebut, maka perlu adanya klasifikasi status rumah tangga yang menerima bantuan PKH dan tidak. Proses pengklasifikasian ini dapat dilakukan menggunakan Analisis Diskriminan. Hasil pengklasifikasian menggunakan analisis diskriminan untuk kasus status penerima bantuan PKH di Provinsi NTB diperoleh nilai APER sebesar 0.2450 yang artinya tingkat kesalahan klasifikasi sebesar 24.5 % atau hasil pengklasifikasian tergolong akurat. &nbsp

Model Regresi Semiparametrik Spline Hasil Produksi Padi di Kabupaten Lombok Timur

Beras merupakan suatu sumber bahan makanan pokok penting yang harus tetap terjaga ketersediannya sepanjang tahun. Namun untuk tahun-tahun terakhir ini Indonesia yang dikenal dengan kekayaan alamnya, menjadi salah satu negara pengimpor beras. Hal ini dikarenakan konsumsi beras di indonesia terus meningkat setiap tahunnya, sedangkan produksi beras yang dihasilkan kurang mencukupi konsumsi masyarakat Indonesia. Penelitian ini dilakukan dengan tujuan untuk menentukan model regresi semiparametrik spline pada analisis faktor-faktor yang mempengaruhi hasil produksi padi di Kabupaten Lombok Timur tahun 2014, serta mengetahui faktor-faktor apa saja yang mempengaruhi hasil produksi padi tersebut. Metode yang digunakan adalah regresi semiparametrik spline dengan pemilihan titik knot optimum menggunakan Generalized Cross Validation. Hasil yang diperoleh menunjukkan bahwa variabel yang secara signifikan mempengaruhi hasil produksi padi adalah ketinggian wilayah dari permukaan laut, dengan nilai koefisien determinasi sebesar 99,71% dan nilai Root Mean Square Error of Prediction sebesar 41,65

Spline and Kernel Mixed Nonparametric Regression for Malnourished Children Model in West Nusa Tenggara

Health sector development is essential to improve human life quality, especially in West Nusa Tenggara (NTB) Province. Based on data from the NTB Provincial Health Office from 2011 to 2016, children under five suffering from malnutrition continued to increase, caused by several factors that affected the incident. Therefore, appropriate analysis is needed to model children who suffer from malnutrition in NTB Province in 2016, consisting of 10 districts based on the variables that influence it. The analysis in this study was carried out using a nonparametric regression mixed-model spline truncated and kernel. The estimation of the nonparametric regression curve depends on the optimal knot points and bandwidths parameter. Therefore, in determining the optimal knot points and bandwidths obtained from Generalized Cross-Validation (GCV). Based on the analysis that has been done, we obtained a nonparametric regression mixed-model spline truncated and kernel optimal knot points, such as  for each variable and optimum bandwidths, such as  and , with  the value of GCV. The mixed model acquired has a good model by considering the values of  and MSE. Besides, the MAPE value indicated a high degree of accuracy, so that the model obtained has an excellent forecast

Kernel Nonparametric Regression for Forecasting Local Original Income

Regional Original Revenue (ROR) is an income collected based on regional regulations under statutory regulations. ROR aims to give authority to Regional Governments to sponsor the implementation of regional autonomy following regional potential. Every year, the Central Lombok Regency government sets ROR targets to assist the government in formulating regional policies. The targets set by the government are sometimes not following their realization. This study aims to determine a model that can be used in forecasting ROR targets. One way to predict the value of ROR is by using a nonparametric regression approach. This approach is flexible since it is not dependent on a particular model. The use of the nonparametric kernel regression method with the Gaussian kernel function obtained a minimum GCV value of 1,769688931 with an optimum bandwidth value of  of 0,212740452 and  of 0,529682589. Modeling with optimum bandwidth produces a coefficient of determination of 87,55%. The best model is used for forecasting and produces a MAPE value of 5,4%. The analysis results show that what influences the value of ROR is ROR receipts in the previous month and the previous 12 months

Analisis Dependensi Faktor Makroekonomi terhadap Tingkat Harga Emas Dunia dengan Pendekatan Copula

Gold is a precious metal that used many times as an alternative investment. Before investing, every investor requires relevant information to make profitable investment decisions. Relevant information can be obtained by looking at the dependency relationship between variables. In identifying the relationship between variables, a Copula approach could be used, since it is not tight against the assumption of normality, which is common in macroeconomic variables. Copula used were Archimedean Copula family, such as Clayton, Frank, and Gumbel.  The results of this study indicated that the Archimedean Copula of the Frank family is the best Copula models to explain the structure of dependencies between gold and each composite stock price index and exchange rate, with each parameter obtained were 2.286 and -2.2390, respectively, while Clayton Copula family was the best Copula models to explain the structure of dependencies between gold and oil, with parameter obtained was 3.4090

Estimasi Parameter Distribusi Mixture Eksponensial dan Weibull dengan Metode Bayesian Markov Chain Monte Carlo

Dalam estimasi parameter, kadangkala terdapat beberapa permasalahan yang menuntut penyelesaian dengan suatu distribusi mixture atau distribusi campuran. Penelitian ini bertujuan untuk menerapkan estimasi parameter distribusi mixture eksponensial dan Weibull pada data simulasi dengan metode estimasi Bayesian Markov Chain Monte Carlo (MCMC). Hasil yang diperoleh menunjukkan bahwa perhitungan analitik estimasi parameter lebih akurat dibandingkan perhitungan dengan bantuan perangkat lunak, apabila dipandang dari segi kesesuaian teori serta proses integrasiny

Factor Extraction and Bicluster Analysis on Halal Destinations in Lombok Island

Indonesia is one of the countries currently developing the concept of halal tourism. Halal tourism includes many variables that are related to each other, which need to be grouped into several main factors that affect tourist visits. This study was conducted to group the variables associated with halal tourism visits to Lombok Island using factor analysis and to classify sub-districts and halal tourism destinations on Lombok Island using the Plaid Bicluster algorithm. Based on the analysis using the main component extraction technique in factor analysis with varimax rotation, it can be concluded that the 9 halal tourism characteristic variables can be grouped into 2 main factors. Furthermore, by using the Plaid Bicluster algorithm, 2 Bicluster were produced. There were 7 sub-districts and 9 destinations formed in Bicluster I, and 8 sub-districts and 3 destinations formed in Bicluster II

Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi

Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method