Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user’s subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discu...

This paper proposes a new two stage least squares (2SLS) estimator which is consistent and asymptotically normal in the presence of many weak and irrelevant instruments and heteroskedasticity. In the first stage the estimator uses an adaptive absolute shrinkage and selection operator (LASSO) that selects the relevant instruments with high probability. However, the adaptive LASSO estimates have ...

Solutions to non-linear least squares problems play an essential role in structure and motion problems in computer vision. The predominant approach for solving these problems is a Newton like scheme which uses the hessian of the function to iteratively find a local solution. Although fast, this strategy inevitably leeds to issues with poor local minima and missed global minima. In this paper ra...

Adaptive algorithms are obligatory for tracking the channel or its inverse in mobile communication systems. Typical are either the Least-Mean-Squares (LMS) algorithm or the Recursive-Least-Squares (RLS) algorithm. Their performance considerably improves when the step-size for LMS (or forgetting factor for RLS) is chosen according to the Doppler speed, that is the speed of a vehicle in which the...

This paper proposes an efficient online learning algorithm to track the smoothing functions of Additive Models. The key idea is to combine the linear representation of Additive Models with a Recursive Least Squares (RLS) filter. In order to quickly track changes in the model and put more weight on recent data, the RLS filter uses a forgetting factor which exponentially weights down observations...

The aim of this paper is to compare through Monte Carlo simulations the finite sample properties of the estimates of the parameters of the weighted exponential distribution obtained by five estimation methods: maximum likelihood, moments, L-moments, ordinary least-squares, and weighted least-squares. The bias and mean-squared error are used as the criterion for comparison. The simulation study ...

The international metrological community is involved in an ongoing series of comparisons of measurement standards. The calculation of differences from a “reference value”, some form of weighted mean of the results, has been recommended as a convenient figure of merit for participating laboratories. In temperature metrology, the uncertainties of the defining reference points are dominated by the...

7 We consider in this paper the distribution of the cumulative size of recessions in 17 capitalist countries over the period 1871–1994, using data on annual percentage changes in real GDP. 9 A recession is de ned as a year in which GDP growth is negative, and the cumulative change is the change from peak to trough during a recession period. We examine both the whole sample 11 and di erent parti...

In order to quantify the transactions in investment,this paper establishes a non-subjective transaction strategy model 
 based on Apriori algorithm.The technical analysis index is blurred through triangular fuzzy device,designing decision system with class rule library,a product inference machine Mamdani meaning,and central average device.The structural parameters of are estimated using re...

In this work, we establish exponential inequalities which allow us to construct confidence regions, characterized by ellipses, for the least squares estimates of parameters in case a linear regression model with ?-mixing errors.