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 regression function with two predictors in the non-parametric model will have two different non-parametric regression functions. Therefore, we...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure is fast and does not require an estimation fo...

estimation of the conceptual costs in construction projects can be regarded as an important issue in feasibility studies. this estimation has a major impact on the success of construction projects. indeed, this estimation supports the required information that can be employed in cost management and budgeting of these projects. the purpose of this paper is to introduce an intelligent model to im...

<span lang="EN-US">A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant and outliers. Therefore, focused analysing comparing impact three variable selection based machine learning techniques, including random forest (RF), support vector machines (SVM), ...

Abstract In recent years, identification of nonlinear dynamical systems from data has become increasingly popular. Sparse regression approaches, such as Identification Nonlinear Dynamics (SINDy), fostered the development novel governing equation algorithms assuming state variables are known {\it a priori} and equations lend themselves to sparse, linear expansions in (nonlinear) basis variables....

Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. Spline approach consists of regression spline and smoothing spline. Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochasti...

Having implemented discrete stationary wavelet transform (DSWT) to an image, combining generalized cross validation (GCV), noise is reduced directly in the high frequency sub-bands which are at the better resolution levels and local contrast is enhanced by combining de-noising method with in-complete Beta transform (IBT) in the high frequency sub-bands which are at the worse resolution levels. ...

We discuss weighted least squares estimates of ill-conditioned linear inverse problems where weights are chosen to be inverse error covariance matrices. Least squares estimators are the maximum likelihood estimate for normally distributed data and parameters, but here we do not assume particular probability distributions. Weights for the estimator are found by ensuring its minimum follows a χ d...