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Can you help me understand the effect of outliers on the semivariogram? For example on p. 653 of Konstantin Krivoruchko's Spatial Statistical Data Analysis for GIS Users, there is a histogram of well depth with the 30 deepest wells highlighted showing the skewness in the histogram. The semivariogram model of well depth for all 333 wells shows mainly a nugget effect and little relationship between gamma and distance. Such a pattern is attributed to deep wells being distributed somewhat randomly through out the study area. Is the combination of outliers having values in the tail of the histogram and the spatial distribution of the outliers being random that causes lack of spatial dependence in the semivariogram? Once the deep wells are removed, then the semivariogram models shows (Figure 15.7) strong dependence. I am struck by the effect on the semivariogram model caused by the statistical distribution and spatial distribution of 10% of the data. Thanks
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11-23-2011
02:50 AM
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Does the GA extension include robust measures to handle outliers when producing sample variograms? Is there something similar to "cressie=TRUE" argument that is available in the R package gstat? Thanks
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11-17-2011
04:48 AM
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Alex, Thanks for posting that simple solution. It was just what I needed.
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11-03-2011
12:26 PM
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The R package spsurvey can draw random samples, as well as spatially balanced probabilistic samples, so you might check out that package. Here is a web site that describes some survey applications using spsurvey: http://www.epa.gov/nheerl/arm/ Mike
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05-23-2011
12:09 PM
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Hi Lauren, Just checking, but the "StdResid" returned with the OLS output are they standardized residuals or Studentized residuals? I opened up the code, which is nice, and saw: self.stdRedisuals = e / self.seResiduals. and seResiduals=sqrt(s2) That looks like a standardized residual as the denominator does not contain the multiplicant of the square root of 1 minus hii. Both Weisberg (1985) and Ramsay and Schafer (2002) show the equation for a studentized residual to be: studresi = resi/(sigma hat*sqrt(1- hii)) Thanks, Mike
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05-03-2011
04:25 AM
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Hi Lauren, Is this the citation for White's heteroscedasticity-consistent standard errors that is being used in producing the robust standard errors for the OLS regression: White, H. (1980), "A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity," Econometrica, 48, 817-838? I am working on a manuscript for a journal article so I need to know how to cite the output produced by OLS regression in ArcMap. Thanks, Mike
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04-19-2011
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I have two other questions about OLS in ArcMap. 1. Is the Kenker (BP) statistic reported indicating biased standard errors the applying the Breusch-Pagan test for heteroskedasticity? I think it is, but can't find any documentation. 2. What method is being used to produce the robust standard error estimates? Thanks, Mike
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03-28-2011
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How does OLS in ArcMap handle dummy variables? If I have a factor that has three levels, 1, 2, and 3, does OLS in ArcMap automatically create the corresponding dummy variables? Or do, I need to create the 2 dummy columns that correspond to the the levels of the factor before I run the OLS regression? Such as: Factor d1 d2 1 0 0 2 1 0 3 0 1 Thanks, Mike
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03-09-2011
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