Resistant selection of the smoothing parameter for smoothing splines
Statistics and Computing, 2001 Robust automatic selection techniques for the smoothing parameter of a smoothing spline are introduced. They are based on a robust predictive error criterion and can be viewed as robust versions of Cp and cross-validation. They lead to smoothing splines which are stable and reliable in terms of mean squared error over a large spectrum of model ...
Cantoni, Eva, Ronchetti, Elvezio
openaire +3 more sources
Analisis Error Terhadap Peramalan Data Penjualan
Techno.Com, 2021 Tujuan penelitian ini membahas tentang peramalan permintaan lampu LED bermerk Sanyo. Penelitian ini menggunakan metode Moving Average dan Exponential Smoothing.
Alyauma Hajjah, Yulvia Nora Marlim
doaj +1 more source
Trend-Smooth: Accelerate Asynchronous SGD by Smoothing Parameters Using Parameter Trends [PDF]
IEEE Access, 2019 Stochastic gradient descent(SGD) is the fundamental sequential method in training large scale machine learning models. To accelerate the training process, researchers proposed to use the asynchronous stochastic gradient descent (A-SGD) method in model learning.
Guoxin Cui +4 more
openaire +2 more sources
Spline estimator and its asymptotic properties in multiresponse nonparametric regression model [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2020 In applications, we often meet the problem where more than one response variable is observed at several values of predictor variables, and these responses are correlated with each other.
Budi Lestari +2 more
doaj +1 more source
Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model [PDF]
Journal of Sciences, Islamic Republic of Iran, 2020 Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The
Anna Islamiyati
doaj +1 more source
A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
doaj +1 more source
PREDIKSI JUMLAH CALON PESERTA DIDIK BARU MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI BROWN
Jurnal Lebesgue, 2020 Peramalan data statistika memerlukan kesesuaian pola data dengan metode peramalan yang digunakan. Tujuan penelitian ini yaitu memprediksi jumlah mahasiswa baru pada tahun ajaran baru menggunakan metode Double Exponential Smoothing satu parameter dari ...
Aden, Anggela Supriyanti
doaj +1 more source
Stochastic Smoothing Methods for Nonsmooth Global Optimization
Кібернетика та комп'ютерні технології, 2020 . The paper presents the results of testing the stochastic smoothing method for global optimization of a multiextremal function in a convex feasible subset of Euclidean space.
V.I. Norkin
doaj +1 more source
Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
doaj +1 more source
Journal of Open Innovation: Technology, Market and Complexity, 2023 This research concentrates on using neural networks in the modelling and prediction of macroeconomic variables in specific. Macroeconomic predictors are particularly interested in neural networks because of their capacity to predict any linear or non ...
Rabia Sabri +5 more
doaj +1 more source