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- Fang Han (2018): An Exponential Inequality for U-Statistics Under Mixing Conditions
Explicit mixing conditions are given for guaranteeing fast convergence, the bound proves to be analogous to the one under independence, and extension to non-stationary time series is straightforward.
RePEc:spr:jotpro:v:31:y:2018:i:1:d:10.1007_s10959-016-0722-4 Save to MyIDEAS - Brendan K. Beare (2007): A New Mixing Condition
In this paper a new mixing condition for sequences of random variables is considered. This mixing condition is termed ã-mixing. Whereas mixing conditions such as á-mixing are typically defined in terms of entire ó-fields of sets generated by random variables in the distant past and future, ã-mixing is defined in terms of a smaller class of sets: the finite dimensional cylinder sets. This leads to a definition of mixing more general than those in current use. A Rosenthal inequality, law of large numbers, and functional central limit theorem are proved for ã-mixing processes.
RePEc:oxf:wpaper:348 Save to MyIDEAS - Liebscher, Eckhard (1999): Asymptotic normality of nonparametric estimators under [alpha]-mixing condition
We assume that the sample is a part of a stationary sequence satisfying an [alpha]-mixing property. The proofs are based on a central limit theorem for [alpha]-mixing triangular arrays in the paper by Liebscher [1996, Stochastics and Stochastics Rep. 59, 241-258].
RePEc:eee:stapro:v:43:y:1999:i:3:p:243-250 Save to MyIDEAS - Takashi Kamihigashi & John Stachurski (2011): An Order-Theoretic Mixing Condition for Monotone Markov Chains
We discuss stability of discrete-time Markov chains satisfying monotonicity and an order-theoretic mixing condition that can be seen as an alternative to irreducibility. A chain satisfying these conditions has at most one stationary distribution.
RePEc:kob:dpaper:dp2011-24 Save to MyIDEAS - Kamihigashi, Takashi & Stachurski, John (2012): An order-theoretic mixing condition for monotone Markov chains
We discuss the stability of discrete-time Markov chains satisfying monotonicity and an order-theoretic mixing condition that can be seen as an alternative to irreducibility. A chain satisfying these conditions has at most one stationary distribution.
RePEc:eee:stapro:v:82:y:2012:i:2:p:262-267 Save to MyIDEAS - Takashi Kamihigashi & John Stachurski (2011): An Order-Theoretic Mixing Condition for Monotone Markov Chains
We discuss stability of discrete-time Markov chains satisfying monotonicity and an order-theoretic mixing condition that can be seen as an alternative to irreducibility. A chain satisfying these conditions has at most one stationary distribution.
RePEc:acb:cbeeco:2011-559 Save to MyIDEAS - Luc, Bauwens & J.V.K., ROMBOUTS (2005): Bayesian inference for the mixed conditional heteroskedasticity model
We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik and Paolelella 2004a).
RePEc:ctl:louvec:2005058 Save to MyIDEAS - Luc Bauwens & Jeroen V.K. Rombouts (2006): Bayesian inference for the mixed conditional heteroskedasticity model
We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik, and Paolella 2004a).
RePEc:iea:carech:0607 Save to MyIDEAS - BAUWENS, Luc & ROMBOUTS, Jeroen V.K. (2005): Bayesian inference for the mixed conditional heteroskedasticity model
We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik, and Paolella 2004a).
RePEc:cor:louvco:2005085 Save to MyIDEAS - L. Bauwens & J.V.K. Rombouts (2007): Bayesian inference for the mixed conditional heteroskedasticity model
We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211--50).
RePEc:ect:emjrnl:v:10:y:2007:i:2:p:408-425 Save to MyIDEAS