the gross calorific value (gcv) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. many researchers have proposed empirical formulas for estimating gcv value of coal. there are some known methods like bomb calorimeter for determining the gcv in the laboratory. but these methods are cumbersome, costly and time consuming. in this paper, m...

The tting of a thin plate smoothing spline to noisy data using the method of minimizing the Generalized Cross Validation (GCV) function is computationally intensive involving the repeated solution of sets of linear systems of equations as part of a minimization routine. In the case of a data set of more than a few hundred points, implementation on a workstation can become unrealistic and it is ...

Noisy data are often fitted using a smoothing parameter, controling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this paramater minimizes the error of the result (as compared to the unknown, exact data), usually expressed in the L2 norm. This optimum cannot be found exact...

The gross calorific value (GCV) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. Many researchers have proposed empirical formulas for estimating GCV value of coal. There are some known methods like Bomb Calorimeter for determining the GCV in the laboratory. But these methods are cumbersome, costly and time consuming. In this paper, m...

Clustering analysis is widely used in many fields. Traditionally clustering is regarded as unsupervised learning for its lack of a class label or a quantitative response variable, which in contrast is present in supervised learning such as classification and regression. Here we formulate clustering as penalized regression with grouping pursuit. In addition to the novel use of a non-convex group...

Spline smoothing provides a powerful tool for estimating nonparametric functions. Most of the past work is based on the assumption that the random errors are independent. Observations are often correlated in applications; e.g., time series data, spatial data and clustered data. It is well known that correlation greatly a ects the selection of smoothing parameters, which are critical to the perf...

small area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  generally, in some cases, small area estimation uses parametric modeling.  but in fact, a lot of models have no linear relationship between the small area average and the covariat...

[1] Estimating CO2 fluxes from the pattern of atmospheric CO2 concentrations with atmospheric transport models is an ill-posed inverse problem, whose solution is stabilized using prior information. Weights assigned to prior information and to CO2 concentrations at different locations are quantified by parameters that are not well known, and differences in the choice of these parameters contribu...

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final (FPE) and Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on most commonly accepted definition of MSPE as expectation squared prediction error loss, we provide theoretical expressions for it, valid any line...

Small Area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  Small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  Generally, in some cases, small area estimation uses parametric modeling.  But in fact, a lot of models have no linear relationship between the small area average and the covariat...