Mechanical models in nonparametric regression

نویسندگان

  • Vladimı́r Balek
  • Ivan Mizera
چکیده

A common motif in the expositions of spline-based methods in statistical smoothing or numerical interpolation is an allusion to mechanical analogies—motivated perhaps by a desire to provide some explanation why the resulting shapes ought to be regarded as “natural”. The univariate case has its Oxford English Dictionary reference to draftsman spline as “a flexible strip of wood or hard rubber used by draftsmen in laying out broad sweeping curves”, which suggests (amiss!) that the eponymous mathematical object shares exactly the same properties. The introduction of “thin-plate spline” in the bivariate domain usually comes with a more distinctive story about the deformation of an elastic flat thin plate—for instance, page 139 of Green and Silverman [14] or page 108 of Small [32]: if the plate is deformed to the shape of the function f , and is small, then the bending energy is (up to the first order) proportional to the smoothing penalty. The importance attached by the scientific community to such trivia varies: while some consider it a signal from Nature, indicating the righteous path in the potentially endless forest of possibilities—see especially Bookstein [3, 4, 5], but also Bookstein and Green [6], Small [32], Dryden and Mardia [10]—for others it is a marginal curiosity, not deserving to stand in the path of the appreciation of computational and theoretical properties. In our case, the desire of the second author to comprehend the connection between thin-plate splines and total variation penalties led him to cross-questioning of the first author, theoretical physicist with principal interests in gravitation and cosmology; the latter reluctantly, but eventually cooperatively descended into the caverns of “engineering”—theory of elastic and plastic behavior of solid bodies. The unveiled connection not only turned out to be interesting, but yielded also a practical return for the second author: the elucidated mechanical models hinted Koenker and Mizera [19] where—that is, in which community—to look for relevant algorithmic solutions for their proposals. Which could be the end of the story were it not for queries that started to come occassionally thereafter, about a text documenting the apocryphal knowledge. So, here finally an attempt to produce one. The available space allows only for an overview of physical facts relevant to nonparametric regression; for somewhat nonstandard physical derivations (albeit perhaps unsurprising for an expert in solid mechanics), we refer to Balek [2].

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Differenced-Based Double Shrinking in Partial Linear Models

Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can ...

متن کامل

A New Nonparametric Regression for Longitudinal Data

In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...

متن کامل

Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data

The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...

متن کامل

A Comparison of Thin Plate and Spherical Splines with Multiple Regression

Thin plate and spherical splines are nonparametric methods suitable for spatial data analysis. Thin plate splines acquire efficient practical and high precision solutions in spatial interpolations. Two components in the model fitting is considered: spatial deviations of data and the model roughness. On the other hand, in parametric regression, the relationship between explanatory and response v...

متن کامل

A MODIFICATION ON RIDGE ESTIMATION FOR FUZZY NONPARAMETRIC REGRESSION

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

متن کامل

A Comparison of the Nonparametric Regression Models using Smoothing Spline and Kernel Regression

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smo...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011