Search results for "60G15"

showing 4 items of 4 documents

Almost sure central limit theorems for random ratios and applications to lse for fractional ornstein–uhlenbeck processes

2012

We investigate an almost sure limit theorem (ASCLT) for sequences of random variables having the form of a ratio of two terms such that the numerator satisfies the ASCLT and the denominator is a positive term which converges almost surely to 1. This result leads to the ASCLT for least square estimators for Ornstein-Uhlenbeck process driven by fractional Brownian motion.

Mathematics::ProbabilityProbability (math.PR)FOS: MathematicsMathematics - Probability60F05 60G15 60H05 60H07
researchProduct

Calibrating Expert Assessments Using Hierarchical Gaussian Process Models

2020

Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background. We follow the fully Bayesian approach (the s…

0106 biological sciencesComputer sciencepäätöksentekoRECONCILIATIONInferencecomputer.software_genre01 natural sciencesSTOCK ASSESSMENTenvironmental management010104 statistics & probabilityJUDGMENTSELICITATIONkalakantojen hoito111 Mathematicstilastolliset mallitReliability (statistics)Applied Mathematicsgaussiset prosessitfisheries sciencebias correctionexpert elicitationPROBABILITY62P1260G15symbols62F15Statistics and ProbabilityarviointimenetelmätBayesian probabilityenvironmental management.Bayesian inferenceMachine learningHEURISTICSsymbols.namesakeasiantuntijatMANAGEMENT0101 mathematicsGaussian processGaussian processCATCH LIMITSbusiness.industrybayesilainen menetelmä010604 marine biology & hydrobiologyUnivariateExpert elicitationOPINIONSupra BayesArtificial intelligenceHeuristicsbusinessFISHERIEScomputerBayesian Analysis
researchProduct

Malliavin Calculus and Skorohod Integration for Quantum Stochastic Processes

2000

A derivation operator and a divergence operator are defined on the algebra of bounded operators on the symmetric Fock space over the complexification of a real Hilbert space $\eufrak{h}$ and it is shown that they satisfy similar properties as the derivation and divergence operator on the Wiener space over $\eufrak{h}$. The derivation operator is then used to give sufficient conditions for the existence of smooth Wigner densities for pairs of operators satisfying the canonical commutation relations. For $\eufrak{h}=L^2(\mathbb{R}_+)$, the divergence operator is shown to coincide with the Hudson-Parthasarathy quantum stochastic integral for adapted integrable processes and with the non-causal…

Statistics and ProbabilityPure mathematics[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Integrable systemComplexificationSpace (mathematics)Malliavin calculus01 natural sciences81S25Fock space81S25; 60H07; 60G15010104 statistics & probabilitysymbols.namesakeOperator (computer programming)60H07FOS: Mathematics0101 mathematicsMathematical PhysicsMathematicsApplied Mathematics010102 general mathematicsProbability (math.PR)Hilbert spaceStatistical and Nonlinear Physics[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Bounded function60G15symbols[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
researchProduct

PARAMETER ESTIMATION FOR FRACTIONAL ORNSTEIN-UHLENBECK PROCESSES: NON-ERGODIC CASE

2011

We consider the parameter estimation problem for the non-ergodic fractional Ornstein-Uhlenbeck process defined as $dX_t=\theta X_tdt+dB_t,\ t\geq0$, with a parameter $\theta>0$, where $B$ is a fractional Brownian motion of Hurst index $H\in(1/2,1)$. We study the consistency and the asymptotic distributions of the least squares estimator $\hat{\theta}_t$ of $\theta$ based on the observation $\{X_s,\ s\in[0,t]\}$ as $t\rightarrow\infty$.

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Probability (math.PR)62F12 60G18 60G1562F12 60G18 60G15.[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Mathematics::ProbabilityFOS: MathematicsParameter estimationYoung integralYoung integral.Parameter estimation; Non-ergodic fractional Ornstein-Uhlenbeck process; Young integral.[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilityNon-ergodic fractional Ornstein-Uhlenbeck process
researchProduct