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We consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between t...
Models for complex systems are often built with more parameters than can be uniquely identified by available data. Because of the variety of causes, identifying a lack of parameter identifiability typ...
The recently introduced two-parameter infinitely-many neutral alleles model extends the celebrated one-parameter version, related to Kingman's distribution, to diffusive two-parameter Poisson-Dirichle...
Consider a linear regression model with n-dimensional response vector, p-dimensional regression parameter beta and independent normally distributed errors. Suppose that the parameter of interest is th...
We consider a reflected Ornstein-Uhlenbeck process driven by a fractional Brownian motion with Hurst parameter $H\in(0,1)$. Our goal is to estimate an unknown drift parameter $\alpha\in (-\infty,\inft...
We explore various estimators for the parameters of a pair-copula construction (PCC), among those the stepwise semiparametric (SSP) estimator, designed for this dependence structure. We present its as...
We propose a simple continuous time model for modeling the lead-lag effect between two financial assets. A two-dimensional process $(X_t,Y_t)$ reproduces a lead-lag effect if, for some time shift $\va...
Our perspective in this paper follows the framework adopted by Lin et al. (2006), who intro- duced several loss functions for the identi cation of the elements of a parameter ensemble that represent...
The LASSO is a variable subset selection procedure in statistical linear regression based on ℓ1 penalization of the least-squares operator. Its behavior crucially depends, both in practice and...

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