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DESCRIPTION:Title: Recovery of functions from moments with applications\n\n
 Speaker: Dr. Robert Mnatsakanov\n\nProfessor\, Department of Mathematics\,
  West Virginia University Yonsei University\n\n  \n\nDate and time:\n\
 nFriday\, January 30\, 2026 \n\n11:00 A.M. – 12:00 P.M. Eastern Time
  \n\nNguyen Engineering Building\, Jajodia Auditorium\, Room 1101 \n\n
 4511 Patriot Circle\, Fairfax\, Virginia 22030\n\nThe seminar talk is 
 also live-streamed.\n\nRegistration Link:\n\n[https://nam11.safelinks.prot
 ection.outlook.com/?url=https%3A%2F%2Fforms.office.com%2Fr%2FgGy9yw9TfP&amp;da
 ta=05%7C02%7Ckhassan1%40gmu.edu%7Cca5d11973e854bdbbe8208de5547d15f%7C9e857
 255df574c47a0c00546460380cb%7C0%7C0%7C639041964178655093%7CUnknown%7CTWFpb
 GZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjo
 iTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&amp;sdata=T%2FKtR%2FRKkA4R3RGyLWJhlBUcb
 3HE5pEzZpmAtBeawk8%3D&amp;reserved=0](https://forms.office.com/pages/responsep
 age.aspx?id=VXKFnlffR0ygwAVGRgOAyyOgzDzxl9BKjPcQSFGnjTpUQlVZQURCNU9UT0xFMk
 wzQUwyT0hQU0FWUy4u&amp;amp\;route=shorturl)\n\nAbstract: \n\nIn this talk th
 e problem of recovering the moment-determinate functions and their derivat
 ives given the information contained in the sequence of moments is discuss
 ed. It is shown how the rate of approximations is related to the number of
  moments used in proposed constructions.\n\nMoment type reconstructions ar
 e of interest in many areas of mathematics and statistics. For example\, a
 s one of the main applications in statistics\, we will show how our approx
 imations\, based on estimated moments of the model\, yields a new type of 
 non-parametric estimates of the quantile\, conditional quantile\, regressi
 on functions\, and the so-called relative distribution function. In additi
 on\, the moment method is applied to the problem of estimating the joint d
 ensity function of the random coefficients in the linear regression model.
  Under the assumptions that coefficients of the regression function are no
 n-negative random variables\, the new non-parametric density estimator of 
 the unknown density function of coefficients is derived.\n\nAbstract:\n\nA
 s an another example\, it is worth mentioning the area of Computed Tomogra
 phy\, where the moment methods are very useful. One can establish the rela
 tionship between the moments of observed Radon transform (projections) of 
 f and the moments of original function f (image) itself for recovery of th
 e image f from the values of its Radon transform. We implemented new algor
 ithm to reconstruct f from the values of its Radon transform. Numerical an
 d graphical convergences of our constructions are illustrated by means of 
 tables and graphs.\n\nBio: \n\nRobert M. Mnatsakanov\, PhD\, is the Prof
 essor of Mathematics at the School of Mathematical and Data Sciences\, Wes
 t Virginia University. He received his PhD in Physics and Mathematics from
  Moscow Institute of Electronics and Mathematics. Mnatsakanov’s research
  interests are concentrated in different areas of statistics and mathemati
 cs\, such as the change-set problem\, entropy estimation in multidimension
 al space\, on recovering the distributions in the framework of multidimens
 ional Hausdorff and Stieltjes moment problems\, the nonparametric estimati
 on of unknown mixing distributions in Poisson mixture models. He works on 
 reconstructions of unknown intensity functions in Computed Tomography by i
 nverting the Radon and the Laplace transforms using newly developed the mo
 ment-recovered approximations.\n\nRoom: Jajodia Auditorium\, Room 1101  
 \, Bldg: Nguyen Engineering Building\, \, George Mason University\, Fairfa
 x\, Virginia\, United States\, 22030
LOCATION:Room: Jajodia Auditorium\, Room 1101  \, Bldg: Nguyen Engineerin
 g Building\, \, George Mason University\, Fairfax\, Virginia\, United Stat
 es\, 22030
ORGANIZER:kafi@ieee.org
SEQUENCE:22
SUMMARY:Recovery of functions from moments with applications 
URL;VALUE=URI:https://events.vtools.ieee.org/m/533225
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;&lt;em&gt;&lt;span
  data-olk-copy-source=&quot;MessageBody&quot;&gt;Title: Recovery of functions from mome
 nts with applications&lt;/span&gt;&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;&amp;nbsp\;&lt;/em&gt;&lt;/strong
 &gt;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;Speaker: Dr. Robert Mnatsakanov
 &lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;&lt;strong&gt;&lt;em&gt;Professor\, Department o
 f Mathematics\, West Virginia University&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&amp;nbsp\;Yonse
 i University&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;  &amp;nbsp\;&lt;/p&gt;\n&lt;p cl
 ass=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;Date and time:&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;x_el
 ementtoproof&quot;&gt;&lt;strong&gt;Friday\, January 30\, 2026&lt;/strong&gt; &amp;nbsp\;&lt;/p&gt;\n&lt;
 p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;11:00 A.M. &amp;ndash\; 12:00 P.M. Eastern 
 Time&lt;/strong&gt; &amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;Nguyen En
 gineering Building\, Jajodia Auditorium\, Room 1101&lt;/strong&gt; &amp;nbsp\;&lt;/p&gt;
 \n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;4511 Patriot Circle\, Fairfax\, Virg
 inia 22030&lt;/strong&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;The 
 seminar talk is also live-streamed.&amp;nbsp\;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;x_ele
 menttoproof&quot;&gt;Registration Link:&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;a
  href=&quot;https://forms.office.com/pages/responsepage.aspx?id=VXKFnlffR0ygwAV
 GRgOAyyOgzDzxl9BKjPcQSFGnjTpUQlVZQURCNU9UT0xFMkwzQUwyT0hQU0FWUy4u&amp;amp\;amp
 \;route=shorturl&quot;&gt;&lt;strong&gt;https://nam11.safelinks.protection.outlook.com/?
 url=https%3A%2F%2Fforms.office.com%2Fr%2FgGy9yw9TfP&amp;amp\;data=05%7C02%7Ckh
 assan1%40gmu.edu%7Cca5d11973e854bdbbe8208de5547d15f%7C9e857255df574c47a0c0
 0546460380cb%7C0%7C0%7C639041964178655093%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0
 eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIj
 oyfQ%3D%3D%7C0%7C%7C%7C&amp;amp\;sdata=T%2FKtR%2FRKkA4R3RGyLWJhlBUcb3HE5pEzZpm
 AtBeawk8%3D&amp;amp\;reserved=0&amp;nbsp\;&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;\n&lt;p class=&quot;x_elementto
 proof&quot;&gt;&lt;strong&gt;&lt;span data-olk-copy-source=&quot;MessageBody&quot;&gt;Abstract:&lt;/span&gt;&lt;/
 strong&gt; &amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;In this talk the proble
 m of recovering the moment-determinate functions and their derivatives giv
 en the information contained in the sequence of moments is discussed. It i
 s shown how the rate of approximations is related to the number of moments
  used in proposed constructions.&lt;/p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;Moment type r
 econstructions are of interest in many areas of mathematics and statistics
 . For example\, as one of the main applications in statistics\, we will sh
 ow how our approximations\, based on estimated moments of the model\, yiel
 ds a new type of non-parametric estimates of the quantile\, conditional qu
 antile\, regression functions\, and the so-called relative distribution fu
 nction. In addition\, the moment method is applied to the problem of estim
 ating the joint density function of the random coefficients in the linear 
 regression model. Under the assumptions that coefficients of the regressio
 n function are non-negative random variables\, the new non-parametric dens
 ity estimator of the unknown density function of coefficients is derived.&lt;
 /p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;&lt;strong&gt;Ab
 stract:&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;x_MsoNormal&quot;&gt;As an another example\, it is
  worth mentioning the area of Computed Tomography\, where the moment metho
 ds are very useful. One can establish the relationship between the moments
  of observed Radon transform (projections) of f and the moments of origina
 l function f (image) itself for recovery of the image f from the values of
  its Radon transform. We implemented new algorithm to reconstruct f from t
 he values of its Radon transform. Numerical and graphical convergences of 
 our constructions are illustrated by means of tables and graphs.&lt;/p&gt;\n&lt;p c
 lass=&quot;x_elementtoproof&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;&lt;strong&gt;B
 io:&lt;/strong&gt; &amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;x_elementtoproof&quot;&gt;Robert M. Mnatsaka
 nov\, PhD\, is the Professor of Mathematics at the School of Mathematical 
 and Data Sciences\, West Virginia University. He received his PhD in Physi
 cs and Mathematics from Moscow Institute of Electronics and Mathematics. M
 natsakanov&amp;rsquo\;s research interests are concentrated in different areas
  of statistics and mathematics\, such as the change-set problem\, entropy 
 estimation in multidimensional space\, on recovering the distributions in 
 the framework of multidimensional Hausdorff and Stieltjes moment problems\
 , the nonparametric estimation of unknown mixing distributions in Poisson 
 mixture models. He works on reconstructions of unknown intensity functions
  in Computed Tomography by inverting the Radon and the Laplace transforms 
 using newly developed the moment-recovered approximations.&lt;/p&gt;
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