摘要:

The results of analyzing experimental data using a parametric model may heavily depend on the chosen model for regression and variance functions, moreover also on a possibly underlying preliminary transformation of the variables. In this paper we propose and discuss a complex procedure which consists in a simultaneous selection of parametric regression and variance models from a relatively rich model class and of Box-Cox variable transformations by minimization of a cross-validation criterion. For this it is essential to introduce modifications of the standard cross-validation criterion adapted to each of the following objectives: 1. estimation of the unknown regression function, 2. prediction of future values of the response variable, 3. calibration or 4. estimation of some parameter with a certain meaning in the corresponding field of application. Our idea of a criterion oriented combination of procedures (which usually if applied, then in an independent or sequential way) is expected to lead to more accurate results. We show how the accuracy of the parameter estimators can be assessed by a “moment oriented bootstrap procedure", which is an essential modification of the “wild bootstrap” of Härdle and Mammen by use of more accurate variance estimates. This new procedure and its refinement by a bootstrap based pivot (“double bootstrap”) is also used for the construction of confidence, prediction and calibration intervals. Programs written in Splus which realize our strategy for nonlinear regression modelling and parameter estimation are described as well. The performance of the selected model is discussed, and the behaviour of the procedures is illustrated,e.g.,by an application in radioimmunological assay.

ISSN:0233-1888    

DOI:10.1080/02331889908802692

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Additive regression models have a long history in nonparametric regression. It is well known that these models can be estimated at the one dimensional rate. Until recently, however, these models have been estimated by a backfitting procedure. Although the procedure converges quickly, its iterative nature makes analyzing its statistical properties difficult. Recently, an integration approach has been studied that allows for the derivation of a closed form for the estimator. Although they seem to be competing procedures for the same problem, their interpretation is in fact different. For none of them the quite important question in economics of derivative estimation has been investigated so far. This paper extends the approach of marginal integration to the simultaneous estimation of both the function and its derivatives by combining the integration procedure with a local polynomial approach. Thus, we additionally get a design adaptive estimator. Finally the merits of this procedure with respect to the estimation of a production function subject to separability conditions are discussed. The procedure is applied to livestock production data from Wisconsin, showing performance and handling of these methods in practice. We demonstratee.g., that there is some evidence of increasing returns to scale for larger farms.This work was first revised in 1995. The research was supported by the Deutsche Forschungsgemeinschaft, SFB 373. The first author, E.Severance-Lossing died in 1996; the mentioned address refers to the second author.

ISSN:0233-1888    

DOI:10.1080/02331889908802693

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Letdenote the class of probability density functions onwith a nonnegative and integrable characteristic function. To each, there is an adjoint density, which is proportional to the characteristic function ofp. The productshave a greatest lower bound Λ, and it is known that 0.5276 < Λ < 0.8571. Several approaches to sharpen these bounds are discussed. In particular, a variational problem is considered, in whichpis supposed to have a certain compactly supported convolution root, and which leads to an improved upper estimate, Λ < 0.8567 … The paper closes with a proposal for a multivariate analogue of the uncertainty relation.

ISSN:0233-1888    

DOI:10.1080/02331889908802694

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

W. Polasek, EDA - Explorative Datenanalyse. Einfuhrung in die deskriptive Statistik. Springer-Verlag, Berlin 1994, ISBN 3-540-58394-7, 345 pp.

ISSN:0233-1888    

DOI:10.1080/02331889908802695

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0233-1888    

DOI:10.1080/02331889908802691

出版商:Gordon & Breach Sceince Publishers    

年代:1999

数据来源: Taylor