Nonparametric Pointwise Estimation for a Regression Model with Multiplicative Noise

In this paper, we consider a general nonparametric regression estimation model with the feature of having multiplicative noise. We propose a linear estimator and nonlinear estimator by wavelet method. The convergence rates of those regression estimators under pointwise error over Besov spaces are proved. It turns out that the obtained convergence rates are consistent with the optimal convergence rate of pointwise nonparametric functional estimation.

Wavelet regression estimation over Lp risk based on negatively associated sample

This paper considers wavelet estimations of a regression function based on negatively associated sample. We provide upper bound estimations over [Formula: see text] risk of linear and nonlinear wavelet estimators in Besov space, respectively. When the random sample reduces to the independent case, our convergence rates coincide with the optimal convergence rates of classical nonparametric regression estimation.

Nonparametric Regression Estimation for Circular Data

Non-parametric regression with a circular response variable and a unidimensional linear regressor is a topic which was discussed in the literature. In this work, we extend the results to the case of multivariate linear explanatory variables. Nonparametric procedures to estimate the circular regression function are formulated. A simulation study is carried out to study the sample performance of the proposed estimators.

PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient equals to 0,954609.