scholarly journals A weak law of large numbers for realised covariation in a Hilbert space setting

Author(s):  
Fred Espen Benth ◽  
Dennis Schroers ◽  
Almut E.D. Veraart
2002 ◽  
Vol 39 (3) ◽  
pp. 566-580 ◽  
Author(s):  
Serge Guillas

In this paper, we consider a Hilbert-space-valued autoregressive stochastic sequence (Xn) with several regimes. We suppose that the underlying process (In) which drives the evolution of (Xn) is stationary. Under some dependence assumptions on (In), we prove the existence of a unique stationary solution, and with a symmetric compact autocorrelation operator, we can state a law of large numbers with rates and the consistency of the covariance estimator. An overall hypothesis states that the regimes where the autocorrelation operator's norm is greater than 1 should be rarely visited.


1988 ◽  
Vol 37 (1-2) ◽  
pp. 91-94
Author(s):  
Anant P. Godbole

We consider a sequence [Formula: see text] of independent Hilbert-space valued random variables and extend the Hoffmann-Jorgensen and Pisier Strong Law of Large Numbers (SLLN) in a way similiar to Teicher's extension of the classical Kolmogorov SLLN.


2002 ◽  
Vol 39 (03) ◽  
pp. 566-580
Author(s):  
Serge Guillas

In this paper, we consider a Hilbert-space-valued autoregressive stochastic sequence (X n ) with several regimes. We suppose that the underlying process (I n ) which drives the evolution of (X n ) is stationary. Under some dependence assumptions on (I n ), we prove the existence of a unique stationary solution, and with a symmetric compact autocorrelation operator, we can state a law of large numbers with rates and the consistency of the covariance estimator. An overall hypothesis states that the regimes where the autocorrelation operator's norm is greater than 1 should be rarely visited.


Author(s):  
Jochen Rau

Statistical mechanics concerns the transition from the microscopic to the macroscopic realm. On a macroscopic scale new phenomena arise that have no counterpart in the microscopic world. For example, macroscopic systems have a temperature; they might undergo phase transitions; and their dynamics may involve dissipation. How can such phenomena be explained? This chapter discusses the characteristic differences between the microscopic and macroscopic realms and lays out the basic challenge of statistical mechanics. It suggests how, in principle, this challenge can be tackled with the help of conservation laws and statistics. The chapter reviews some basic notions of classical probability theory. In particular, it discusses the law of large numbers and illustrates how, despite the indeterminacy of individual events, statistics can make highly accurate predictions about totals and averages.


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