the first step to quantify crop phenology is precise estimation of the parameters, which affect it. these parameters are mainly temperature and photoperiod. this study was conducted with eight sowing dates to quantify response of stem elongation rate (ser) to temperature and photoperiod in wheat (tajan cultivar). the regression models fitted to ser against temperature were flat, logistic, quadr...

in this study, a total of 101,147 monthly test day records of somatic cell count collected from 13,977 first lactation holstein cows (in 183 herds) calved between 2002 and 2006 were used. for the genetic analysis, a random regression test day model was utilized. somatic cell score (scs) was calculated based on natural logarithm of somatic cell count. in the model, fixed effect of contemporary g...

In geotechnical engineering, rock mechanics and engineering geology, depending on the project design, uniaxial strength and static Youngchr('39')s modulus of rocks are of vital importance. The direct determination of the aforementioned parameters in the laboratory, however, requires intact and high-quality cores and preparation of their specimens have some limitations. Moreover, performing thes...

Optimal bandwidths for local polynomial regression usually involve functionals of the derivatives of the unknown regression function. In the multivariate case, estimates of these functionals are not readily available, primarily because estimating multivariate derivatives is complicated. In this paper, an estimator of multivariate second derivative is obtained via local quadratic regression with...

L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many pract...

Fuzzy regression models has been traditionally considered as a problem of linear programming. The use of quadratic programming allows to overcome the limitations of linear programming as well as to obtain highly adaptable regression approaches. However, we verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this prob...

Quadratic programming is concerned with minimizing a convex quadratic function subject to linear inequality constraints. The variables are assumed to be nonnegative. The unique solution of quadratic programming (QP) problem (QPP) exists provided that a feasible region is non-empty (the QP has a feasible space). A method for searching for the solution to a QP is provided on the basis of statisti...

In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two different parameterizations of the model and investigate local optimal designs with respect to the c-, Dand Ecriteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is d...

We use the sinc kernel to construct an estimator for the integrated squared regression function. Asymptotic normality of the estimator at different rates is established, depending on whether the regression function vanishes or not.

The standard SVR formulation for real-valued function approximation on multidimensional spaces is based on the -insensitive loss function, where errors are considered not correlated. Due to this, local information in the feature space which can be useful to improve the prediction model is disregarded. In this paper we address this problem by defining a generalized quadratic loss where the co-oc...