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Statistical Inference for Stochastic Processes
Journal Prestige (SJR): 0.322
Citation Impact (citeScore): 1
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-9311 - ISSN (Online) 1387-0874
Published by Springer-Verlag Homepage  [2467 journals]
  • High-dimensional estimation of quadratic variation based on penalized
           realized variance

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      Abstract: Abstract In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous Itô semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is—with a high probability—the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven subsampling procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three–five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV—and also RV—of full rank.
      PubDate: 2022-12-05
       
  • On the $$\alpha $$ -lazy version of Markov chains in estimation and
           testing problems

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      Abstract: Abstract Given access to a single long trajectory generated by an unknown irreducible Markov chain M, we simulate an \(\alpha \) -lazy version of M which is ergodic. This enables us to generalize recent results on estimation and identity testing, that were stated for ergodic Markov chains, in a way that allows fully empirical inference. In particular, our approach shows that the pseudo spectral gap introduced by Paulin (Electron J Probab 20:32, 2015) and defined for ergodic Markov chains may be given a meaning already in the case of irreducible but possibly periodic Markov chains.
      PubDate: 2022-12-05
       
  • On the integrated mean squared error of wavelet density estimation for
           linear processes

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      Abstract: Abstract Let \(\{X_n: n\in {{\mathbb {N}}}\}\) be a linear process with density function \(f(x)\in L^2({{\mathbb {R}}})\) . We study wavelet density estimation of f(x). Under some regular conditions on the characteristic function of innovations, we achieve, based on the number of nonzero coefficients in the linear process, the minimax optimal convergence rate of the integrated mean squared error of density estimation. Considered wavelets have compact support and are twice continuously differentiable. The number of vanishing moments of mother wavelet is proportional to the number of nonzero coefficients in the linear process and to the rate of decay of characteristic function of innovations. Theoretical results are illustrated by simulation studies with innovations following Gaussian, Cauchy and chi-squared distributions.
      PubDate: 2022-11-17
       
  • Large deviation inequalities of Bayesian estimator in nonlinear regression
           models

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      Abstract: Abstract In the present paper, we establish some large deviation inequalities of the Bayesian estimator for the nonlinear regression model under the conditions of dependent errors which extend the results in Jeganathan (J Multivar Anal 30(2):227–240, 1989) from independent errors and dependent sequences. As an application, we give an large deviation inequality for the Michaelis–Menten model.
      PubDate: 2022-10-29
       
  • Finite-sample properties of estimators for first and second order
           autoregressive processes

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      Abstract: Abstract The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily available and routinely fitted using freely available statistical software like R. A potential problem is that commonly applied estimators for the coefficients of AR processes are severely biased when the time series are short. This paper studies the finite-sample properties of well-known estimators for the coefficients of stationary AR(1) and AR(2) processes and provides bias-corrected versions of these estimators which are quick and easy to apply. The new estimators are constructed by modeling the relationship between the true and originally estimated AR coefficients using weighted orthogonal polynomial regression, taking the sampling distribution of the original estimators into account. The finite-sample distributions of the new bias-corrected estimators are approximated using transformations of skew-normal densities, combined with a Gaussian copula approximation in the AR(2) case. The properties of the new estimators are demonstrated by simulations and in the analysis of a real ecological data set. The estimators are easily available in our accompanying R-package for AR(1) and AR(2) processes of length 10–50, both giving bias-corrected coefficient estimates and corresponding confidence intervals.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-021-09262-4
       
  • Improved estimation method for high dimension semimartingale regression
           models based on discrete data

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      Abstract: Abstract In this paper we study a high dimension (Big Data) regression model in continuous time observed in the discrete time moments with dependent noises defined by semimartingale processes. To this end an improved (shrinkage) estimation method is developed and the non-asymptotic comparison between shrinkage and least squares estimates is studied. The improvement effect for the shrinkage estimates showing the significant advantage with respect to the "small" dimension case is established. It turns out that obtained improvement effect holds true uniformly over observation frequency. Then, a model selection method based on these estimates is developed. Non-asymptotic sharp oracle inequalities for the constructed model selection procedure are obtained. Constructive sufficient conditions for the observation frequency providing the robust efficiency property in adaptive setting without using any sparsity assumption are found. A special stochastic calculus tool to guarantee these conditions for non-Gaussian Ornstein–Uhlenbeck processes is developed. Monte-Carlo simulations for the numeric confirmation of the obtained theoretical results are given.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-021-09258-0
       
  • On minimax robust testing of composite hypotheses on Poisson process
           intensity

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      Abstract: Abstract The problem on the minimax testing of a Poisson process intensity is considered. For a given disjoint sets \({{\mathcal {S}}}_T\) and \({{\mathcal {V}}}_T\) of possible intensities \({{\mathbf {s}}}_{T}\) and \({{\mathbf {v}}}_{T}\) , respectively, the minimax testing of the composite hypothesis \(H_{0}: {{\mathbf {s}}_T} \in {{\mathcal {S}}}_T\) against the composite alternative \(H_{1}: {{\mathbf {v}}_T} \in {{\mathcal {V}}}_T\) is investigated. It is assumed that a pair of intensities \({{\mathbf {s}}_T^{0}} \in {{\mathcal {S}}}_T\) and \({{\mathbf {v}}_T^{0}} \in {{\mathcal {V}}}_T\) are chosen, and the “Likelihood-Ratio” test for intensities \({{\mathbf {s}}_T^{0}}\) and \({{\mathbf {v}}_T^{0}}\) is used for testing composite hypotheses \(H_{0}\) and \(H_{1}\) . The case, when the 1-st kind error probability \(\alpha \) is fixed and we are interested in the minimal possible 2-nd kind error probability \(\beta ({{\mathcal {S}}}_T,{{\mathcal {V}}}_T)\) , is considered. What are the maximal sets \({{\mathcal {S}}}({{\mathbf {s}}}_{T}^{0},{{\mathbf {v}}}_{T}^{0})\) and \({{\mathcal {V}}}({{\mathbf {s}}}_{T}^{0},{{\mathbf {v}}}_{T}^{0})\) , which can be replaced by the pair of intensities \(({{\mathbf {s}}_T^{0}},{{\mathbf {v}}_T^{0}})\) without essential loss for testing performance ' In the asymptotic case ( \(T\rightarrow \infty \) ) those maximal sets \({{\mathcal {S}}}({{\mathbf {s}}}_{T}^{0},{{\mathbf {v}}}_{T}^{0})\) and \({{\mathcal {V}}}({{\mathbf {s}}}_{T}^{0},{{\mathbf {v}}}_{T}^{0})\) are described.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-021-09265-1
       
  • Randomized consistent statistical inference for random processes and
           fields

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      Abstract: Abstract We propose a randomized approach to the consistent statistical analysis of random processes and fields on \({\mathbb {R}}^m\) and \({\mathbb {Z}}^m, m=1,2,...\) , which is valid in the case of strong dependence: the parameter of interest \(\theta \) only has to possesses a consistent sequence of estimators \({\hat{\theta }}_n\) . The limit theorem is related to consistent sequences of randomized estimators \({\hat{\theta }}_n^*\) ; it is used to construct consistent asymptotically efficient sequences of confidence intervals and tests of hypotheses related to the parameter \(\theta \) . Upper bounds for “admissible” sequences of normalizing coefficients in the limit theorem are established for some statistical models in Part 2.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-022-09270-y
       
  • Weak convergence of nonparametric estimators of the multidimensional and
           multidimensional-multivariate renewal functions on Skorohod topology
           spaces

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      Abstract: Abstract This paper deals with the weak convergence of nonparametric estimators of the multidimensional and multidimensional-multivariate renewal functions on Skorohod topology spaces. It is an extension of Harel et al. (J Math Anal Appl 189:240–255, 1995) from the one-dimensional case to the multivariate and multidimensional case. The estimators are based on a sequence of non-negative independent and identically distributed (iid) random vectors. They are expressed as infinite sums of k-folds convolutions of the empirical distribution function. Their weak convergence study heavily rests on that of the empirical distribution function.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-021-09263-3
       
  • Optimal linear interpolation of multiple missing values

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      Abstract: Abstract The problem of linear interpolation in the context of a multivariate time series having multiple (possibly non-consecutive) missing values is studied. A concise formula for the optimal interpolating filter is derived, and illustrations using two simple models are provided.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-022-09269-5
       
  • A Lepskiĭ-type stopping rule for the covariance estimation of
           multi-dimensional Lévy processes

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      Abstract: Abstract We suppose that a Lévy process is observed at discrete time points. Starting from an asymptotically minimax family of estimators for the continuous part of the Lévy Khinchine characteristics, i.e., the covariance, we derive a data-driven parameter choice for the frequency of estimating the covariance. We investigate a Lepskiĭ-type stopping rule for the adaptive procedure. Consequently, we use a balancing principle for the best possible data-driven parameter. The adaptive estimator achieves almost the optimal rate. Numerical experiments with the proposed selection rule are also presented.
      PubDate: 2022-10-01
      DOI: 10.1007/s11203-021-09264-2
       
  • Wavelet eigenvalue regression in high dimensions

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      Abstract: Abstract In this paper, we construct the wavelet eigenvalue regression methodology (Abry and Didier in J Multivar Anal 168:75–104, 2018a; in Bernoulli 24(2):895–928, 2018b) in high dimensions. We assume that possibly non-Gaussian, finite-variance p-variate measurements are made of a low-dimensional r-variate ( \(r \ll p\) ) fractional stochastic process with non-canonical scaling coordinates and in the presence of additive high-dimensional noise. The measurements are correlated both time-wise and between rows. Building upon the asymptotic and large scale properties of wavelet random matrices in high dimensions, the wavelet eigenvalue regression is shown to be consistent and, under additional assumptions, asymptotically Gaussian in the estimation of the fractal structure of the system. We further construct a consistent estimator of the effective dimension r of the system that significantly increases the robustness of the methodology. The estimation performance over finite samples is studied by means of simulations.
      PubDate: 2022-09-18
      DOI: 10.1007/s11203-022-09279-3
       
  • Robust and efficient specification tests in Markov-switching
           autoregressive models

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      Abstract: Abstract This study develops two types of robust test statistics applicable to Markov-switching autoregressive models. The test statistics can be constructed by sum functionals of the “smoothed” probabilities that a given observation came from a particular regime and do not require the estimation of additional parameters. Monte Carlo experiments show that the tests have good finite-sample size and power properties. The tests are applied to investigate the fluctuations in real GNP growth in the U.S.
      PubDate: 2022-08-30
      DOI: 10.1007/s11203-022-09277-5
       
  • On Stein’s lemma in hypotheses testing in general non-asymptotic
           case

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      Abstract: Abstract The problem of testing two simple hypotheses in a general probability space is considered. For a fixed type-I error probability, the best exponential decay rate of the type-II error probability is investigated. In regular asymptotic cases (i.e., when the length of the observation interval grows without limit) the best decay rate is given by Stein’s exponent. In the paper, for a general probability space, some non-asymptotic lower and upper bounds for the best rate are derived. These bounds represent pure analytic relations without any limiting operations. In some natural cases, these bounds also give the convergence rate for Stein’s exponent. Some illustrating examples are also provided.
      PubDate: 2022-08-24
      DOI: 10.1007/s11203-022-09278-4
       
  • Weak-convergence of empirical conditional processes and conditional
           U-processes involving functional mixing data

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      Abstract: Abstract U-statistics represent a fundamental class of statistics arising from modeling quantities of interest defined by multi-subject responses. U-statistics generalize the empirical mean of a random variable X to sums over every m-tuple of distinct observations of X. W. Stute [Ann. Probab. 19 (1991) 812–825] introduced a class of so-called conditional U-statistics, which may be viewed as a generalization of the Nadaraya-Watson estimates of a regression function. Stute proved their strong pointwise consistency to : $$\begin{aligned} m(\mathbf { t}):=\mathbb {E}[\varphi (Y_{1},\ldots ,Y_{m}) (X_{1},\ldots ,X_{m})=\mathbf {t}], ~~\text{ for }~~\mathbf { t}\in \mathcal {X}^{m}. \end{aligned}$$ In this paper we are mainly interested in establishing weak convergence of conditional U-processes in a functional mixing data framework. More precisely, we investigate the weak convergence of the conditional empirical process indexed by a suitable class of functions and of conditional U-processes when the explicative variable is functional. We treat the weak convergence in both cases when the class of functions is bounded or unbounded satisfying some moment conditions. These results are proved under some standard structural conditions on the Vapnik-Chervonenkis classes of functions and some mild conditions on the model. The theoretical results established in this paper are (or will be) key tools for many further developments in functional data analysis.
      PubDate: 2022-07-25
      DOI: 10.1007/s11203-022-09276-6
       
  • Estimation of stationary probability of semi-Markov Chains

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      Abstract: Abstract This paper concerns the estimation of stationary probability of ergodic semi-Markov chains based on an observation over a time interval. We derive asymptotic properties of the proposed estimator, when the time of observation goes to infinity, as consistency, asymptotic normality, law of iterated logarithm and rate of convergence in a functional setting. The proofs are based on asymptotic results on discrete-time semi-Markov random evolutions.
      PubDate: 2022-07-01
      DOI: 10.1007/s11203-021-09255-3
       
  • Martingale estimation functions for Bessel processes

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      Abstract: Abstract In this paper we derive martingale estimating functions for the dimensionality parameter of a Bessel process based on the eigenfunctions of the diffusion operator. Since a Bessel process is non-ergodic and the theory of martingale estimating functions is developed for ergodic diffusions, we use the space-time transformation of the Bessel process and formulate our results for a modified Bessel process. We deduce consistency, asymptotic normality and discuss optimality. It turns out that the martingale estimating function based of the first eigenfunction of the modified Bessel process coincides with the linear martingale estimating function for the Cox Ingersoll Ross process. Furthermore, our results may also be applied to estimating the multiplicity parameter of a one-dimensional Dunkl process and some related polynomial processes.
      PubDate: 2022-07-01
      DOI: 10.1007/s11203-021-09250-8
       
  • Detection and identification of changes of hidden Markov chains:
           asymptotic theory

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      Abstract: Abstract This paper revisits a unified framework of sequential change-point detection and hypothesis testing modeled using hidden Markov chains and develops its asymptotic theory. Given a sequence of observations whose distributions are dependent on a hidden Markov chain, the objective is to quickly detect critical events, modeled by the first time the Markov chain leaves a specific set of states, and to accurately identify the class of states that the Markov chain enters. We propose computationally tractable sequential detection and identification strategies and obtain sufficient conditions for the asymptotic optimality in two Bayesian formulations. Numerical examples are provided to confirm the asymptotic optimality.
      PubDate: 2022-07-01
      DOI: 10.1007/s11203-021-09253-5
       
  • Adaptive tests for parameter changes in ergodic diffusion processes from
           discrete observations

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      Abstract: Abstract We consider the adaptive test for the parameter change in discretely observed ergodic diffusion processes based on the cusum test. Using two test statistics based on the two quasi-log likelihood functions of the diffusion parameter and the drift parameter, we perform the change point tests for both diffusion and drift parameters of the diffusion process. It is shown that the test statistics have the limiting distribution of the sup of the norm of a Brownian bridge. Simulation results are illustrated for the 1-dimensional Ornstein-Uhlenbeck process.
      PubDate: 2022-07-01
      DOI: 10.1007/s11203-021-09249-1
       
  • A chi-square type test for time-invariant fiber pathways of the brain

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      Abstract: Abstract A longitudinal diffusion tensor imaging (DTI) study on a single brain can be remarkably useful to probe white matter fiber connectivity that may or may not be stable over time. We consider a novel testing problem where the null hypothesis states that the trajectories of a coherently oriented fiber population remain the same over a fixed period of time. Compared to other applications that use changes in DTI scalar metrics over time, our test is focused on the partial derivative of the continuous ensemble of fiber trajectories with respect to time. The test statistic is shown to have the limiting chi-square distribution under the null hypothesis. The power of the test is demonstrated using Monte Carlo simulations based on both the theoretical and empirical critical values. The proposed method is applied to a longitudinal DTI study of a normal brain.
      PubDate: 2022-04-11
      DOI: 10.1007/s11203-022-09268-6
       
 
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