Bayesian smoothing spline with dependency models

The smoothing spline model is widely used for fitting a smooth curve because of its flexibility and smoothing properties. Our study is motivated by estimating the long-term trend of the U.S. unemployment level. In this dissertation, a class of Bayesian smoothing spline with dependency models is developed. The unemployment level and other labor-force time series, which are often used to analyze market and economic conditions, are strongly in uenced by seasonality, as well as irregular or short-term fluctuations. We apply the basic Bayesian smoothing spline model to obtain the smooth estimation of the trend from a time series, which captures the fundamental tendency of general economic expansions and contractions. We further generalize the basic Bayesian smoothing spline model with dependence structure. This generalization significantly improves the boundary performance and elevates the overall accuracy and precision by borrowing information from different cycles. Finally, we construct the multivariate Bayesian smoothing spline with dependency model, which enables us to estimate the trends of the unemployment and employment level simultaneously. The accuracy and precision are improved by the joint model

Model Smoothing Spline Dengan Error Berkorelasi (Studi Kasus: Jumlah Wisatawan Mancanegara Di Bandara Juanda)

Model smoothing spline merupakan teknik spline yang dibantu dengan parameter pemulus. Smoothing spline sangat bergantung pada estimator parameter smoothing, sehingga pemilihan parameter smoothing penting dalam menemukan estimator yang tepat pada smoothing spline. Penelitian ini menggunakan model smoothing spline yang diestimasi dengan menggunakan Generalized Maximum Likelihood (GML) untuk memperoleh model regresi jumlah wisatawan mancanegara (wisman). Model Smoothing spline dengan error berkorelasi adalah model yang didalamnya terdapat error yang berkorelasi yang ditunjukkan dengan matriks kovarian sebagai pembobotnya. Model Smoothing spline dengan error berkorelasi diterapkan pada data jumlah wisatawan mancanegara di bandara Juanda pada tahun 2000 hingga 2015. Tujuan dari penelitian ini adalah mengestimasi parameter penghalus GML serta memprediksi jumlah wisman di bandara Juanda. Hasil estimasi parameter model Smoothing spline dengan error berkorelasi diperoleh model f t ˆ = Td + c . Selanjutnya untuk model Smoothing spline dengan error berkorelasi pada jumlah wisatawan mancanegara di bandara Juanda diperoleh nilai GML sebesar 0,2134811 dan estimasi sigma sebesar 4052,581 dengan derajat bebas 2 ======================================================================================================== Model smoothing spline is a spline assisted technique smoothing parameter. Smoothing spline is highly dependent on the smoothing parameter estimator, thus smoothing parameter selection is important in finding the right estimator at smoothing spline. This study uses a model smoothing spline estimated using Generalized Maximum Likelihood (GML) to obtain the regression model number of foreign tourists. Smoothing spline models with correlated error in which there is a model that correlates the error covariance matrix as indicated by weight. Smoothing spline models with correlated error applied to the data on the number of foreign tourists at Juanda airport in 2000 to 2015. The purpose of this study is to estimate the smoothing parameter GML and to predict the number of tourists at Juanda airport. The results of the model parameter estimation smoothing spline with correlated error model is f t ˆ = Td + c . Furthermore, for smoothing spline models with correlated errors in the number of foreign tourists at Juanda airport GML values obtained by 0,2134811 and sigma estimate of 4052,581 with 2 degrees of freedom

Semiparametric estimation with profile algorithm for longitudinal binary data

This article considers analyzing longitudinal binary data semiparametrically and proposing GEE-Smoothing spline in the estimation of parametric and nonparametric components. The method is an extension of the parametric generalized estimating equation to semiparametric. The nonparametric component is estimated by smoothing spline approach, i.e., natural cubic spline. We use profile algorithm in the estimation of both parametric and nonparametric components. Properties of the estimators are evaluated by simulation

Descriptive Seasonal Adjustment by Minimizing Perturbations

The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes non-stochastic deviations (perturbations). This interpretation gives rise to a critique of the seasonality criterion used there. A new seasonality criterion is proposed that avoids these shortcomings, and the resulting seasonal adjustment method is givenseasonal adjustment; seasonality; smoothing; spline; descriptive decomposition