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Metro: measuring error on simplified surfaces
by P. Cignoni , C. Rocchini , R. Scopigno , 1998
"... ..."
Abstract - Cited by 362 (6 self) - Add to MetaCart
Measurement Errors
by Surjit S. Bhalla, The Pennanent
"... bl ic Di sc lo su re A ut ho riz ed Pu bl ic Di sc lo su re A ut ho riz ed Pu bl ic Di sc lo su re A ut ho riz ed Pu bl ic Di sc lo su re A ut ho riz ed ..."
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with Measurement Error
by Sourafel Girma, Abbi M. Kedir, Sourafel Girma, Abbi M. Kedir
"... in the School of Economics at the University of Nottingham. It aims to promote research in all aspects of economic development and international trade on both a long term and a short term basis. To this end, CREDIT organises seminar series on Development Economics, acts as a point for collaborative ..."
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Nonlinear Models of Measurement​Errors
by Xiaohong Chen, Han Hong, Denis Nekipelov - JOURNAL OF ECONOMIC LITERATURE , 2011
"... Measurement errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. While linear errors-in-variables models are usually handled with ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
Divergence measures based on the Shannon entropy
by Jianhua Lin - IEEE Transactions on Information theory , 1991
"... Abstract-A new class of information-theoretic divergence measures based on the Shannon entropy is introduced. Unlike the well-known Kullback divergences, the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions in-volved. More importantly, ..."
Abstract - Cited by 666 (0 self) - Add to MetaCart
A Model for Measurement Error for Gene Expression Arrays
by David M. Rocke, Blythe Durbin - Journal of Computational Biology , 2001
"... We introduce a model for measurement error in gene expression arrays as a function of the expression level. This model, together with analysis methods, data transformations, and weighting, allows much more precise comparisons of gene expression, and provides guidance for analysis of background, dete ..."
Abstract - Cited by 150 (9 self) - Add to MetaCart
A review of methods for the assessment of prediction errors in conservation presence/absence models.
by Alan H Fielding , John F Bell , Alan H Fielding , John F Bell - Environmental Conservation , 1997
"... Summary Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negativ ..."
Abstract - Cited by 463 (1 self) - Add to MetaCart
Quantile Regression with Measurement Error,”
by Ying Wei , Raymond J Carroll - Journal of the American Statistical Association, , 2009
"... Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by construct ..."
Abstract - Cited by 15 (2 self) - Add to MetaCart
Model-Based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application
by Cheng Li, Wing Hung Wong , 2001
"... Background: A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of ..."
Abstract - Cited by 775 (28 self) - Add to MetaCart
Stable signal recovery from incomplete and inaccurate measurements​,”
by Emmanuel J Candès , Justin K Romberg , Terence Tao - Comm. Pure Appl. Math., , 2006
"... Abstract Suppose we wish to recover a vector x 0 ∈ R m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax 0 + e; A is an n × m matrix with far fewer rows than columns (n m) and e is an error term. Is it possible to recover x 0 accurately based on the data y? To r ..."
Abstract - Cited by 1397 (38 self) - Add to MetaCart
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