Bayesian Estimation of the Spectral Density of a Time Series

By Nidhan Choudhuri, Subhashis Ghosal, Anindya Roy. 2004

144 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5d4df8fdc2d724be56adfc06d41e8f98b7467da7

How researchers see this paper

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

In Artificial Intelligence

1. Judith Rousseau
2. A. van der Vaart
3. Subhashis Ghosal
4. I. Castillo
5. Ravi Ramamoorthi
6. Kolyan Ray
7. Johannes Schmidt-Hieber
8. Cosma Rohilla Shalizi

In Finance

1. Judith Rousseau
2. A. van der Vaart
3. Subhashis Ghosal
4. Cosma Rohilla Shalizi
5. Marc Hoffmann
6. Ravi Ramamoorthi
7. Taeryon Choi
8. Johannes Schmidt-Hieber

In Statistics and Probability

1. Judith Rousseau
2. A. van der Vaart
3. Subhashis Ghosal
4. I. Castillo
5. Richard Nickl
6. Ravi Ramamoorthi
7. Johannes Schmidt-Hieber
8. Kolyan Ray

Citations in the corpus

• On adaptive posterior concentration rates (2013, methodology): “These studies include density estimation in the case of independent and identically distributed observations as in [15], nonparametric regression (Ghosal and van der Vaart [16]) and the white noise model (Zhao [31], Belitser and Ghosal [3]), Markov models (Tang and Ghosal [29]), Gaussian time series”
• Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, background): “For instance, see Tang and Ghosal (2007) for posterior consistency of Bayesian nonparametric estimates with transition kernel of an ergodic Markov process and Choudhuri et al. (2004) for the estimation of the spectral density of”
• Dynamics of Bayesian Updating with Dependent Data and Misspecified Models (2009, background): “These contributions also make (d) certain dynamical assumptions about the data-generating process, most often that it is IID [4, 26, 65] (in this setting, [27] and [59] in particular consider convergence rates), independent non-identically distributed [13, 29], or, in some cases, Markovian [28, 29];”
• Convergence rates of posterior distributions for non-i.i.d. observations (2007, methodology): “As a concrete prior, we consider the prior used by Choudhuri, Ghosal and Roy [7], namely f = τq, where τ = var(Xt) has a nonsingular prior density and q, a probability density on [0,1], is given the Dirichlet–Bernstein prior of Petrone [24].”
• Remarks on consistency of posterior distributions (2008, background): “ easily applied in the non-identically distributed case. As in Section 1, the posterior can be written as the ratio of two integrals. A stronger form of KL support (for instance, see Choudhuri et al. [9] and Amewou-Atisso et al. [1]) takes care of the denominator. It is not clear if there is a simpl”