Error models for reducing history match bias

نویسندگان
چکیده

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

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Reducing Bias in Production Speech Models

Replacing hand-engineered pipelines with endto-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and latency constraints of production systems put end-to-end speech models back into the underfitting regime and expose biases in the model that we show cannot be overcome by “scaling up”, i.e., training bigger models on m...

متن کامل

TESTING FOR AUTOCORRELATION IN UNEQUALLY REPLICATED FUNCTIONAL MEASUREMENT ERROR MODELS

In the ordinary linear models, regressing the residuals against lagged values has been suggested as an approach to test the hypothesis of zero autocorrelation among residuals. In this paper we extend these results to the both equally and unequally replicated functionally measurement error models. We consider the equally and unequally replicated cases separately, because in the first case the re...

متن کامل

Instrumental variables vs. grouping approach for reducing bias due to measurement error.

Attenuation of the exposure-response relationship due to exposure measurement error is often encountered in epidemiology. Given that error cannot be totally eliminated, bias correction methods of analysis are needed. Many methods require more than one exposure measurement per person to be made, but the `group mean OLS method,' in which subjects are grouped into several a priori defined groups f...

متن کامل

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bia...

متن کامل

Reducing aggregation error in spatial interaction models by location sampling

Models of spatial interaction such as transport, migration, commuting and trade usually partition space into zones, to represent the receiving and sending end of the interaction. When zones encompass multiple locations, the partitioning causes an aggregation error (Hillsman and Rhoda 1978). The aggregation error increases with the size of zones. Aggregation errors can cause bias (Goodchild 1979...

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

ژورنال

عنوان ژورنال: Computational Geosciences

سال: 2006

ISSN: 1420-0597,1573-1499

DOI: 10.1007/s10596-006-9027-5