The method of truncated total least squares [2] is an alternative to the classical truncated singular value decomposition used for the regularization of ill-conditioned linear systems Ax ≈ b [3]. Truncation methods aim at limiting the contribution of noise or rounding errors by cutting off a certain number of terms in an expansion such as the singular value decomposition. To this end a truncati...

Nonparametric regression approaches are used when the shape of curve between response variable and predictor is assumed to be unknown. excess has high flexibility. A frequently nonparametric approach a truncated spline that excellent ability handle data whose behavior at certain sub-intervals. The aim this study was obtain best model multivariable with linear quadratic using Generalized Cross V...

Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable example is the determination of the smoothness parameter in splines. When the data are generated by a state space model, like in the spline case, efficient algorithms are available to evaluate the GCV score with comple...

Stocks are one of the most popular financial instruments traded in capital market. One stock prices fluctuate up and down due to influence several factors, which is inflation. property real estate sectors important indicators determine level a country economy. Data on case longitudinal data economic field. The divided into 2 parts, namely sample from January 2016 October 2020 out November Decem...

The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present ...

Abstract Discrete ill-posed inverse problems arise in various areas of science and engineering. The presence noise the data often makes it difficult to compute an accurate approximate solution. To reduce sensitivity computed solution noise, one replaces original problem by a nearby well-posed minimization problem, whose is less sensitive than problem. This replacement known as regularization. W...