Kendall's Advanced Theory of Statistics.

By Adrienne W. Kemp, A. Stuart, J. Keith Ord. 1994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Variational Bayesian Reinforcement Learning with Regret Bounds (2018, methodology): “3 Cumulant generating function A fundamental object of statistics that we make use of in this work is the cumulant generating function of a random variable [42], which for random variable X is defined as KX(β) = logE exp(Xβ), (2) i.”
Is the Property of Being Positively Correlated Transitive? (2001, background): “Because of the result on page 511 of Stuart and Ord (1994), this is probably the proof of their Problem 15.”
Shannon Entropy, Renyi Entropy, and Information (2004, background): “In addition, Kendall [10] defines the information content of a probability distribution in the discrete case as (see Section 5.1) Iq(P ) = 1 q − 1 − N∑ i=1 pqi q − 1 which again tends to the Shannon entropy as q → 1.”
χ2 time-frequency discriminator for gravitational wave detection (2004, unknown): “In the stationary Gaussian case, for fixed T and T , the expected value, variance σ(2), and standard deviation σ of the non-central χ(2) distribution with 2p− 2 degrees of freedom are [40] 〈χ(2)〉 = 2p− 2 + λ = 2p− 2 + κ|〈z〉|(2), (8.”
Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation (2007, background): “A pivotal quantity, sometimes termed a pivot, is a quantity whose probability distribution does not depend on any unknown parameters (e.g., Casella and Berger 2002, Chapter 9; Stuart, Ord, and Arnold 1999, Chapter 19).”
Bayesian Inference and Optimal Design in the Sparse Linear Model (2007, methodology): “It is important to note that the linear model is typically employed with a Gaussian prior P (a), which due to conjugacy allows for simple analytic treatment (see [9], Chap.”
Vines: A new graphical model for dependent random variables (2002, methodology): “The use of the minimum information principle to motivate the use of a distribution with given correlation coefficient fits into the long-standing tradition established by Jaynes (see [12, 9]) in which subjective distributions are specified using moment information from an expert by maximizing entrop”
Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations (2012, methodology): “See O’Hagan and Forster (2004) for a review of Bayes factors and alternative methods for Bayesian model choice.”
Extending conventional priors for testing general hypotheses in linear models (2007, unknown): “M1 : {Nn(y | X̃τ̃ , σ(2)In) : C̃ t τ̃ = 0} vs M2 : Nn(y | X̃τ̃ , σ(2)In), (18)”