Variance function

Results: 272



#Item
21Help file for the function mcmc.dp.specd The model is y[i,j] ~ N(mu[i,j],sigma_e^2), where mu[i,j] has mean sum(X[i,j,]*beta), variance sigma_s^2, and correlation corresponding to the mixture of nt non-central Matern cor

Help file for the function mcmc.dp.specd The model is y[i,j] ~ N(mu[i,j],sigma_e^2), where mu[i,j] has mean sum(X[i,j,]*beta), variance sigma_s^2, and correlation corresponding to the mixture of nt non-central Matern cor

Add to Reading List

Source URL: www4.stat.ncsu.edu

- Date: 2013-04-28 14:26:44
    2213:TOPIC Edgeworth Expansions. Let X1 be a random variable with characteristic function ψ1 , mean E(X1 ) = 0, variance E(X12 ) = 1, and E(|X|r+2 ) < ∞ for some integer r ≥ 0. Then X1 has cumulants

    13:TOPIC Edgeworth Expansions. Let X1 be a random variable with characteristic function ψ1 , mean E(X1 ) = 0, variance E(X12 ) = 1, and E(|X|r+2 ) < ∞ for some integer r ≥ 0. Then X1 has cumulants

    Add to Reading List

    Source URL: galton.uchicago.edu

    Language: English - Date: 2002-03-31 23:48:15
      23Modelling Changes in the Unconditional Variance of Long Stock Return Series Cristina Amado∗ University of Minho and NIPE Campus de Gualtar, Braga, Portugal

      Modelling Changes in the Unconditional Variance of Long Stock Return Series Cristina Amado∗ University of Minho and NIPE Campus de Gualtar, Braga, Portugal

      Add to Reading List

      Source URL: creates.au.dk

      Language: English - Date: 2012-02-14 01:27:27
      24Exercises for Stat II 1. Let X1 , ..., Xn be a sample of iid random variables with the density f (x) = (θ+1)xθ , 0 ≤ x ≤ 1. (a) Find the asymptotic variance of the mle of θ. 2. Let X1 , ..., Xn be a iid sample wit

      Exercises for Stat II 1. Let X1 , ..., Xn be a sample of iid random variables with the density f (x) = (θ+1)xθ , 0 ≤ x ≤ 1. (a) Find the asymptotic variance of the mle of θ. 2. Let X1 , ..., Xn be a iid sample wit

      Add to Reading List

      Source URL: cwick.co.nz

      Language: English - Date: 2014-03-25 19:42:02
      25This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and shar

      This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and shar

      Add to Reading List

      Source URL: lab.rockefeller.edu

      Language: English - Date: 2014-12-19 13:26:08
      26IA Probability Examples Sheet 4, Lent 2008 W. T. G.

      IA Probability Examples Sheet 4, Lent 2008 W. T. G.

      Add to Reading List

      Source URL: www.dpmms.cam.ac.uk

      Language: English - Date: 2008-03-08 04:16:54
      27C ONTRIBUTED RESEARCH ARTICLE 1 Manipulation of Discrete Random Variables with discreteRV

      C ONTRIBUTED RESEARCH ARTICLE 1 Manipulation of Discrete Random Variables with discreteRV

      Add to Reading List

      Source URL: journal.r-project.org

      Language: English - Date: 2015-05-06 11:20:02
      28IA Probability Examples Sheet 2, LentW. T. G. 1. A coin with probability p of heads is tossed n times. Let E be the event “a head is obtained on the first toss2 and let Fk be the event “exactly k

      IA Probability Examples Sheet 2, LentW. T. G. 1. A coin with probability p of heads is tossed n times. Let E be the event “a head is obtained on the first toss2 and let Fk be the event “exactly k

      Add to Reading List

      Source URL: www.dpmms.cam.ac.uk

      Language: English - Date: 2009-02-07 05:01:03
      29R Beginner’s Reference Version of July 16, 2014 Basics help(command) opens documentation of command.

      R Beginner’s Reference Version of July 16, 2014 Basics help(command) opens documentation of command.

      Add to Reading List

      Source URL: www.ling.uni-potsdam.de

      Language: English - Date: 2014-07-15 18:06:08
      30Chapter 1 Heuristics The official dogma on parametric estimation is: Good estimators converge to the right thing and have limiting normal distributions; moreover, the variance of the limiting distribution can’t be sma

      Chapter 1 Heuristics The official dogma on parametric estimation is: Good estimators converge to the right thing and have limiting normal distributions; moreover, the variance of the limiting distribution can’t be sma

      Add to Reading List

      Source URL: www.stat.yale.edu

      Language: English - Date: 2010-09-07 00:00:23