AI-GEOSTATS: moving averages and trend
Maybe I'm getting old...there was something wrong also in the past mail! Please consider this one. Sorry for the wrong mails. Dear list members I'm always fighting with the decomposition of trend and residuals or more generally I need to decompose my signal in high and low frequency variability. Without coming to wavelets techniques a simple way is to use moving window averages to obtain a smoothed signal and removing it from the original signal to obtain the residuals. In this way the size of the window let you choose at which level of detail to perform the analysis, i.e. smaller the window higher the frequency of the signal you want to study. But working with moving averages I realize (well, I know that this is not so a big new!) that performing moving window simply doing averages gives a smoothed signal that has some noise; differently if a use some kind of kernel function (also very simple such the one used by Grigov et al. geostatistical Mapping with continuos moving neighborhood, mathematical geology vol 36 no. 2, 2004 see page 273), things work really better (and using a kernel of that type is like calculating moving windows averages not on the original signal but iteratively on moving averages calculated in smaller windows). Now my question is: which is the reason for choosing a specific shape of the kernel Sebastiano + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
Re: AI-GEOSTATS: moving averages and trend
Dear Seba, As far as I know, the shape of the kernel is not that important. Any kernel will yield approximately the same results as long as the scaling parameters (bandwidth) are equivalent. There are some theoretical as well as practical reasons for choosing finite (compact) kernels - the practical reasons for big/huge problems become very important ones. Jos M. En/na seba ha escrit: Maybe I'm getting old...there was something wrong also in the past mail! Please consider this one. Sorry for the wrong mails. Dear list members I'm always fighting with the decomposition of trend and residuals or more generally I need to decompose my signal in high and low frequency variability. Without coming to wavelets techniques a simple way is to use moving window averages to obtain a smoothed signal and removing it from the original signal to obtain the residuals. In this way the size of the window let you choose at which level of detail to perform the analysis, i.e. smaller the window higher the frequency of the signal you want to study. But working with moving averages I realize (well, I know that this is not so a big new!) that performing moving window simply doing averages gives a smoothed signal that has some noise; differently if a use some kind of kernel function (also very simple such the one used by Grigov et al. "geostatistical Mapping with continuos moving neighborhood", mathematical geology vol 36 no. 2, 2004 see page 273), things work really better (and using a kernel of that type is like calculating moving windows averages not on the original signal but iteratively on moving averages calculated in smaller windows). Now my question is: which is the reason for choosing a specific shape of the kernel Sebastiano + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ -- --- Jos M. Blanco-Moreno Dept. de Biologia Vegetal (Botnica) Facultat de Biologia Universitat de Barcelona Av. Diagonal 645 08028 Barcelona SPAIN --- phone: (+34) 934 039 863 fax: (+34) 934 112 842 + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
Re: AI-GEOSTATS: moving averages and trend
Hi José Thank you for your reply. Effectively I'm trying to figure out the theoretical reasons for their use. Bye Sebas At 12.30 01/02/2010, José M. Blanco Moreno wrote: Dear Seba, As far as I know, the shape of the kernel is not that important. Any kernel will yield approximately the same results as long as the scaling parameters (bandwidth) are equivalent. There are some theoretical as well as practical reasons for choosing finite (compact) kernels - the practical reasons for big/huge problems become very important ones. José M. En/na seba ha escrit: Maybe I'm getting old...there was something wrong also in the past mail! Please consider this one. Sorry for the wrong mails. Dear list members I'm always fighting with the decomposition of trend and residuals or more generally I need to decompose my signal in high and low frequency variability. Without coming to wavelets techniques a simple way is to use moving window averages to obtain a smoothed signal and removing it from the original signal to obtain the residuals. In this way the size of the window let you choose at which level of detail to perform the analysis, i.e. smaller the window higher the frequency of the signal you want to study. But working with moving averages I realize (well, I know that this is not so a big new!) that performing moving window simply doing averages gives a smoothed signal that has some noise; differently if a use some kind of kernel function (also very simple such the one used by Grigov et al. geostatistical Mapping with continuos moving neighborhood, mathematical geology vol 36 no. 2, 2004 see page 273), things work really better (and using a kernel of that type is like calculating moving windows averages not on the original signal but iteratively on moving averages calculated in smaller windows). Now my question is: which is the reason for choosing a specific shape of the kernel Sebastiano + + To post a message to the list, send it to mailto:ai-geost...@jrc.ec.europa.euai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/http://www.ai-geostats.org/ -- --- José M. Blanco-Moreno Dept. de Biologia Vegetal (Botànica) Facultat de Biologia Universitat de Barcelona Av. Diagonal 645 08028 Barcelona SPAIN --- phone: (+34) 934 039 863 fax: (+34) 934 112 842 + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
Re: AI-GEOSTATS: moving averages and trend
Hi José Thank you for the interesting references. I'm going to give a look! Bye Sebastiano At 15.46 01/02/2010, José M. Blanco Moreno wrote: Hello again, I am not a mathematician, so I never worried too much on the theoretical reasons. You may be able to find some discussion on this subject in Eubank, R.L. 1999. Nonparametric Regression and Spline Smoothing, 2a ed. M. Dekker, New York. You may be also interested on searching information in and related to (perhaps citing) this work: Altman, N. 1990. Kernel smoothing of data with correlated errors. Journal of the American Statistical Association, 85: 749-759. En/na seba ha escrit: Hi José Thank you for your reply. Effectively I'm trying to figure out the theoretical reasons for their use. Bye Sebas
Re: AI-GEOSTATS: moving averages and trend
Yes you can. Look at my paper http://home.comcast.net/~pgoovaerts/BIOFERTarticle.pdf Fig. 14, right column. The EC local component is the nuggt effect component and the graph below shows the transect of electrical conductivity values after filtering of that microscale component. You can find other examples in the refereed publication section of my website . Cheers, Pierre On Mon, Feb 1, 2010 at 9:17 PM, M. Nur Heriawan mn_heria...@yahoo.comwrote: Goovaerts, regarding the factorial kriging you mentioned below...is it possible to filter the micro component (nugget effect) from our spatial model? Because the magnitude of nugget effect is related to the magnitude of variance error as well. Thank you. Regards, --- M. Nur Heriawan Earth Resources Exploration Research Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung (ITB) Jl. Ganesha 10 Bandung 40132 INDONESIA http://www.mining.itb.ac.id/heriawan -- *From:* Pierre Goovaerts goovae...@terraseer.com *To:* seba sebastiano.trevis...@libero.it *Cc:* José M. Blanco Moreno jmbla...@ub.edu; ai-geostats@jrc.it *Sent:* Tue, February 2, 2010 12:27:09 AM *Subject:* Re: AI-GEOSTATS: moving averages and trend well Factorial Kriging Analysis allows you to tailor the filtering weights to the spatial patterns in your data. You can use the same filter size but different kriging weights depending on whether you want to estimate the local or regional scales of variability. Pierre 2010/2/1 seba sebastiano.trevis...@libero.it Hi José Thank you for the interesting references. I'm going to give a look! Bye Sebastiano At 15.46 01/02/2010, José M. Blanco Moreno wrote: Hello again, I am not a mathematician, so I never worried too much on the theoretical reasons. You may be able to find some discussion on this subject in Eubank, R.L. 1999. Nonparametric Regression and Spline Smoothing, 2a ed. M. Dekker, New York. You may be also interested on searching information in and related to (perhaps citing) this work: Altman, N. 1990. Kernel smoothing of data with correlated errors. Journal of the American Statistical Association, 85: 749-759. En/na seba ha escrit: Hi José Thank you for your reply. Effectively I'm trying to figure out the theoretical reasons for their use. Bye Sebas -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/ -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/
Re: AI-GEOSTATS: moving averages and trend
Goovaerts, regarding the factorial kriging you mentioned below...is it possible to filter the micro component (nugget effect) from our spatial model? Because the magnitude of nugget effect is related to the magnitude of variance error as well. Thank you. Regards,--- M. Nur Heriawan Earth Resources Exploration Research Group Faculty of Mining and Petroleum Engineering Institut Teknologi Bandung (ITB) Jl. Ganesha 10 Bandung 40132 INDONESIA http://www.mining.itb.ac.id/heriawan From: Pierre Goovaerts goovae...@terraseer.com To: seba sebastiano.trevis...@libero.it Cc: José M. Blanco Moreno jmbla...@ub.edu; ai-geostats@jrc.it Sent: Tue, February 2, 2010 12:27:09 AM Subject: Re: AI-GEOSTATS: moving averages and trend well Factorial Kriging Analysis allows you to tailor the filtering weights to the spatial patterns in your data. You can use the same filter size but different kriging weights depending on whether you want to estimate the local or regional scales of variability. Pierre 2010/2/1 seba sebastiano.trevis...@libero.it Hi José Thank you for the interesting references. I'm going to give a look! Bye Sebastiano At 15.46 01/02/2010, José M. Blanco Moreno wrote: Hello again, I am not a mathematician, so I never worried too much on the theoretical reasons. You may be able to find some discussion on this subject in Eubank, R.L. 1999. Nonparametric Regression and Spline Smoothing, 2a ed. M. Dekker, New York. You may be also interested on searching information in and related to (perhaps citing) this work: Altman, N. 1990. Kernel smoothing of data with correlated errors. Journal of the American Statistical Association, 85: 749-759. En/na seba ha escrit: Hi José Thank you for your reply. Effectively I'm trying to figure out the theoretical reasons for their use. Bye Sebas -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/
