empirical models are important tools for relating field-measured biophysical variables to remotely sensed data. regression analysis has been a popular empirical method of linking these two types of data to estimate variables such as biomass, percent vegetation canopy cover, and bare soil. this study was conducted in a semi-arid rangeland ecosystem of qazvin province, iran. this paper presents t...

Accumulation of soluble salts in arid areas which are similar to most regions of Iran is inevitable in soil surface and profile because of low precipitation and high evaporation. High concentration of soluble salts in soil profile caused severe problems for root water uptake thus plant growth stopped. Reducing soil salinity to optimized content by leaching and avoiding soil pounding must be con...

 In this paper we consider a general setting of skew-symmetric distribution which was constructed by Azzalini (1985), and its properties are presented. A suitable empirical estimator for a skew-symmetric distribution is proposed. In data analysis, by comparing this empirical model with the estimated skew-normal distribution, we show that the proposed empirical model has a better fit in den...

in the course of this study, empirical models were developed for the pressure-density relationship in a large cubic baler for baling alfalfa and barley straw. least squares regression analysis was employed to develop the empirical model and estimate the model coefficients by minimizing the summation of the squared differences between data resulting from the developed empirical model and the cor...

Abstract: Model selection is often performed by empirical risk minimization. The quality of selection in a given situation can be assessed by risk bounds, which require assumptions both on the margin and the tails of the losses used. Starting with examples from the 3 basic estimation problems, regression, classification and density estimation, we formulate risk bounds for empirical risk minimiz...

Economic and financial processes are complex and highly nonlinear. However, somewhat surprisingly, linear models like ARMAXGARCH often describe these processes reasonably well. In this paper, we provide a possible explanation for the empirical success of these models. 1 Formulation of the Problem Economic and financial processes are very complex. It is well know that economic and financial proc...

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature, surface wind stress, and sea level analysis. The Markov models are trained in the 1980–95 period and are verified in the 1964–79 period. It is found that the Markov models that include seasonality fit to the data better in ...