The Experts below are selected from a list of 363 Experts worldwide ranked by ideXlab platform
C I Chang - One of the best experts on this subject based on the ideXlab platform.
-
A Complete Sufficient Statistic for Finite-State Markov Processes with Application to Source Coding
2008Co-Authors: L B Wolfe, C I Chang, Senior MemberAbstract:Abstract-A Complete Sufficient Statistic is presented in this paper for the class of all finite-state, finite-order stationary discrete Markov pro-cesses. This Sufficient Statistic is Complete in the sense that it summarizes in entirety the whole of the relevant information supplied by any process sample. The Sufficient Statistic has application to source coding problems such as source matching and calculation of the rate distortion function. Index Terms-Complete Sufficient Statistic, markov chain, source cod-ing. I
-
a Complete Sufficient Statistic for finite state markov processes with application to source coding
IEEE Transactions on Information Theory, 1993Co-Authors: L B Wolfe, C I ChangAbstract:A Complete Sufficient Statistic is presented for the class of all finite-state, finite-order stationary discrete Markov processes. This Sufficient Statistic is Complete in the sense that it summarizes in entirety the whole of the relevant information supplied by any process sample. The Sufficient Statistic has application to source coding problems such as source matching and calculation of the rate distortion function. >
L B Wolfe - One of the best experts on this subject based on the ideXlab platform.
-
A Complete Sufficient Statistic for Finite-State Markov Processes with Application to Source Coding
2008Co-Authors: L B Wolfe, C I Chang, Senior MemberAbstract:Abstract-A Complete Sufficient Statistic is presented in this paper for the class of all finite-state, finite-order stationary discrete Markov pro-cesses. This Sufficient Statistic is Complete in the sense that it summarizes in entirety the whole of the relevant information supplied by any process sample. The Sufficient Statistic has application to source coding problems such as source matching and calculation of the rate distortion function. Index Terms-Complete Sufficient Statistic, markov chain, source cod-ing. I
-
a Complete Sufficient Statistic for finite state markov processes with application to source coding
IEEE Transactions on Information Theory, 1993Co-Authors: L B Wolfe, C I ChangAbstract:A Complete Sufficient Statistic is presented for the class of all finite-state, finite-order stationary discrete Markov processes. This Sufficient Statistic is Complete in the sense that it summarizes in entirety the whole of the relevant information supplied by any process sample. The Sufficient Statistic has application to source coding problems such as source matching and calculation of the rate distortion function. >
Senior Member - One of the best experts on this subject based on the ideXlab platform.
-
A Complete Sufficient Statistic for Finite-State Markov Processes with Application to Source Coding
2008Co-Authors: L B Wolfe, C I Chang, Senior MemberAbstract:Abstract-A Complete Sufficient Statistic is presented in this paper for the class of all finite-state, finite-order stationary discrete Markov pro-cesses. This Sufficient Statistic is Complete in the sense that it summarizes in entirety the whole of the relevant information supplied by any process sample. The Sufficient Statistic has application to source coding problems such as source matching and calculation of the rate distortion function. Index Terms-Complete Sufficient Statistic, markov chain, source cod-ing. I
Hamburg Univ. Inst. Fuer Mathematische Stochastik - One of the best experts on this subject based on the ideXlab platform.
-
Minimal Sufficient Statistics in location-scale parameter models
1999Co-Authors: Mattner L., Hamburg Univ. Inst. Fuer Mathematische StochastikAbstract:Let f be a probability density on the real line, let n be any positive integer, and assume the condition (R) that log f is locally integrable with respect to Lebesgue measure. The either log f is almost everywhere equal to a polynomial of degree less than n, or the order Statistic of n independent and identically distributed observations from the location-scale parameter model generated by f is minimal Sufficient. It follows, subject to (R) and n#<=#3, that a Complete Sufficient Statistic exists in the normal case only. Also, for f with (R) infinitely divisible but not normal, the order Statistic is always minimal Sufficient for the corresponding location-scale parameter model. The proof of the main result uses a theorem on the harmonic analysis of translation and dilation invariant function spaces, attributable to K.O. Leland (1968) and L. Schwartz (1947). (orig.)Available from TIB Hannover: RR 9140(99-08) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman
Lutz Mattner - One of the best experts on this subject based on the ideXlab platform.
-
Minimal Sufficient Statistics in Location-Scale Parameter Models
Springer, 2000Co-Authors: Lutz MattnerAbstract:Let f be a probability density on the real line, let n be any positive integer, and assume the condition (R) that log f is locally integrable with respect to Lebesgue measure. Then either log f is almost everywhere equal to a polynomial of degree less than n, or the order Statistic of n independent and identically distributed observations from the location-scale parameter model generated by f is minimal Sufficient. It follows, subject to (R) and n 3, that a Complete Sufficient Statistic exists in the normal case only. Also, for f with (R) infinitely divisible but not normal, the order Statistic is always minimal Sufficient for the corresponding location-scale parameter model. The proof of the main result uses a theorem on the harmonic analysis of translation and dilation invariant function spaces, attributable to K.O. Leland (1968) and L. Schwartz (1947). 1 Introduction and main results 1.1 Aim. Perhaps the most natural first step in the analysis of a Statistical model consists in de..