The least absolute shrinkage and selection operator (LASSO) is a widely used statistical methodology for simultaneous estimation and variable selection. It is a shrinkage estimation method that allows one to select parsimonious models. In other words, this method estimates the redundant...

We consider a multidimensional Itô process Y=(Yt)t∈[0,T] with some unknown drift coefficient process bt and volatility coefficient σ(Xt,θ) with covariate process X=(Xt)t∈[0,T], the function σ(x,θ) being known up to θ∈Θ. For this model, we consider a change point problem for the...

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This paper proposes consistent and asymptotically Gaussian estimators for the parameters <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\lambda , \sigma $$</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$H$$</EquationSource> </InlineEquation> of the discretely observed fractional Ornstein–Uhlenbeck process solution of the stochastic differential equation <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$d Y_t=-\lambda Y_t dt + \sigma d W_t^H$$</EquationSource> </InlineEquation>, where <InlineEquation ID="IEq4"> <EquationSource...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem,...

The LASSO is a widely used statistical methodology for simultaneous estimation and variable selection. In the last years, many authors analyzed this technique from a theoretical and applied point of view. We introduce and study the adaptive LASSO problem for discretely observed ergodic diffusion...

In this paper we propose the use of $\phi$-divergences as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process $\de X_t = b(X_t, \theta)\de t + \sigma(X_t, \theta)\de W_t$, from discrete observations $\{X_{t_i}, i=0, \ldots, n\}$ with $t_i =...

In this paper we introduce the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in applications like average treatment effect estimation in observational studies. RRP is a Monte Carlo method that randomly generates non-empty recursive...

A one dimensional diffusion process $X=\{X_t, 0\leq t \leq T\}$ is observed only when its path lies over some threshold $\tau$. On the basis of the observable part of the trajectory, the problem is to estimate finite dimensional parameter in both drift and diffusion coefficient under a discrete...

A new procedure to identify grading practice is proposed. In our approach, grading practice are given in terms of a categorical variable whilst usually in the literature, coefficients of the regression line which models school grades as a function of students' achievement, are taken as...