Minimax designs for approximately linear regression

نویسنده

  • Douglas P. Wiens
چکیده

We consider the approximately linear regression model E b 1x1 = I(x) 0 + f(x), XE S, where f(x) is a non-linear disturbance restricted only by a bound on its &(S) norm, and where S is the design space. For loss functions which are monotonic functions of the mean squared error matrix, we derive a theory to guide in the construction of designs which minimize the maximum (over f) loss. We then specialize to the case zT(.r) = (I,x~), so that the fitted surface is a plane. In this case we give minimax designs for loss functions corresponding to the classical D-, A-, E-, Qand G-optimality criteria. AMS Subject ClassiJication: Primary 62K05; secondary 62F35, 62505.

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

Robust designs for approximately linear regression: M-estimated parameters

We obtain designs, to be used for investigations of response surfaces by regression techniques, when (i) the fitted, linear (in the parameters) response is incorrect and (ii) the parameters are to be estimated robustly. Minimax designs are determined for ‘small’ departures from the fitted response. We specialize to the case in which the experimenter fits a plane, when in fact the true response ...

متن کامل

Minimax Regression Designs for Approximately Linear Models with Autocorrelated Errors

We study the construction of regression designs, when the random errors are autocorrelated. Our model of dependence assumes that the spectral density g(~o) of the error process is of the form g ( o ) = (1 -a)go(~O ) + ~gl(o), where go(CO) is uniform (corresponding to uncorrelated errors), ct ~ [0, 1) is fixed, and gx(to) is arbitrary. We consider regression responses which are exactly, or only ...

متن کامل

New criteria for robust integer-valued designs in linear models

We investigate the problem of designing for linear regression models, when the assumed model form is only an approximation to an unknown true model, using two novel approaches. The first is based on a notion of averaging of the mean-squared error of predictions over a neighbourhood of contaminating functions. The other is based on the usual D-optimal criterion but subject to bias-related constr...

متن کامل

Designs for approximately linear regression: Two optimality properties of uniform designs

Absfracr: We study regression designs, with an eye to maximizing the minimum power of the standard test for Lack of Fit. The minimum is taken over a broad class of departures from the assumed multiple linear regression model. We show that the uniform design is maximin. This design attains its optimality by maximizing the minimum bias in the regression estimate of 0’. It is thus surprising that ...

متن کامل

Robust prediction and extrapolation designs for misspecified generalized linear regression models

We study minimax robust designs for response prediction and extrapolation in biased linear regression models. We extend previous work of others by considering a nonlinear fitted regression response, by taking a rather general extrapolation space and, most significantly, by dropping all restrictions on the structure of the regressors. Several examples are discussed. © 2007 Elsevier B.V. All righ...

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 2001