TITLE

Conditional bias-penalized kriging (CBPK)

AUTHOR(S)
Seo, Dong-Jun
PUB. DATE
January 2013
SOURCE
Stochastic Environmental Research & Risk Assessment;Jan2013, Vol. 27 Issue 1, p43
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Simple and ordinary kriging, or SK and OK, respectively, represent the best linear unbiased estimator in the unconditional sense in that they minimize the unconditional (on the unknown truth) error variance and are unbiased in the unconditional mean. However, because the above properties hold only in the unconditional sense, kriging estimates are generally subject to conditional biases that, depending on the application, may be unacceptably large. For example, when used for precipitation estimation using rain gauge data, kriging tends to significantly underestimate large precipitation and, albeit less consequentially, overestimate small precipitation. In this work, we describe an extremely simple extension to SK or OK, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes conditional bias in addition to unconditional error variance. For comparative evaluation of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the kriging estimates are cross-validated. For generalization and potential application in other optimal estimation techniques, we also derive CBPK in the framework of classical optimal linear estimation theory.
ACCESSION #
84486539

 

Related Articles

  • Application and Comparison of Robust Linear Regression Methods for Trend Estimation. Muhlbauer, Andreas; Spichtinger, Peter; Lohmann, Ulrike // Journal of Applied Meteorology & Climatology;Sep2009, Vol. 48 Issue 9, p1961 

    In this study, robust parametric regression methods are applied to temperature and precipitation time series in Switzerland and the trend results are compared with trends from classical least squares (LS) regression and nonparametric approaches. It is found that in individual time series...

  • Combine Estimate of Regression Coefficients. Shiqing Wang; Xiaohua Li // Advances in Information Sciences & Service Sciences;Nov2012, Vol. 4 Issue 21, p533 

    For linear regression models which have the problem of multicollinearity, the ordinary least squares estimate is not a good estimate of regression coefficients. So many have been done to circumvent this problem, one of them is proposing some biased estimate. In the paper, based on the least...

  • Second-order least-squares estimation for regression models with autocorrelated errors. Rosadi, Dedi; Peiris, Shelton // Computational Statistics;Oct2014, Vol. 29 Issue 5, p931 

    In their recent paper, Wang and Leblanc (Ann Inst Stat Math 60:883-900, ) have shown that the second-order least squares estimator (SLSE) is more efficient than the ordinary least squares estimator (OLSE) when the errors are independent and identically distributed with non zero third moments. In...

  • Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? Archfield, S. A.; Pugliese, A.; Castellarin, A.; Skøien, J. O.; Kiang, J. E. // Hydrology & Earth System Sciences;2013, Vol. 17 Issue 4, p1575 

    In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be...

  • Terrestrial climate variability and seasonality changes in the Mediterranean region between 15000 and 4000 years BP deduced from marine pollen records. Dormoy, I.; Peyron, O.; Nebout, N. Combourieu; Goring, S.; Kotthoff, U.; Magny, M.; Pross, J. // Climate of the Past;2009, Vol. 5 Issue 4, p615 

    Pollen-based climate reconstructions were performed on two high-resolution pollen marines cores from the Alboran and Aegean Seas in order to unravel the climatic variability in the coastal settings of the Mediterranean region between 15 000 and 4000 years BP (the Lateglacial, and early to...

  • ACOUSTIC ECHO CANCELLATION USING WAVELET TRANSFORM AND ADAPTIVE FILTERS. FAGARAS, Bianca Alexandra; CONTAN, Cristian; TOPA, Marina Dana // Acta Technica Napocensis. Electronica-Telecomunicatii;Jun2013, Vol. 54 Issue 2, p7 

    The paper proposes a new method for acoustic echo cancellation(AEC), in order to improve the performances of the normalized least-mean-square (NLMS) and non-parametric variable-step-size-NLMS (NPVSS-NLMS) algorithms. The acoustic system is modeled using an impulse response measured in a low...

  • Improved Estimation Using Exponential Estimators for The Population Variance Using Auxiliary Information. Yadav, Subhash Kumar; Pandey, Himanshu; Tripathi, Devendra Dutt // International Transactions in Applied Sciences;Jul2011, Vol. 3 Issue 3, p345 

    In this paper an improved family of exponential estimators, for the estimation of population variance of the variable under study using known values of certain parameters on auxiliary information has been proposed. The expressions for its bias and mean squared error (MSE) have been obtained upto...

  • Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children. Martin, David; Schittenhelm, Jan; Thodberg, Hans; Martin, David D; Thodberg, Hans Henrik // Pediatric Radiology;Feb2016, Vol. 46 Issue 2, p263 

    Background: An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate.Objective: To validate the...

  • Pseudo Two-hop Distributed Consensus with Noise. PENG Huanxin; LIU Bin; WANG Wenkai // Applied Mechanics & Materials;2014, Issue 511-512, p393 

    In this paper, we analyze the pseudo two-hop distributed consensus algorithm with communication noise. If there is communication noise among agents, the convergence performance of distributed consensus algorithms degrades. Supposing that the communication noises are zero-mean Gaussian white...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics