The Annals of Statistics, ISSN 0090-5364, 12/2009, Volume 37, Issue 6B, pp. 4254 - 4278

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with...

Penalty function | Estimation bias | Dimensionality | Covariance | Threshing | Eigenvalues | Longitudinal data | Covariance matrices | Estimators | Perceptron convergence procedure | Covariance matrix | Nonconcave penalized likelihood | Asymptotic normality | High-dimensionality | Sparsistency | Consistency | REGRESSION | sparsistency | STATISTICS & PROBABILITY | LONGITUDINAL DATA | consistency | nonconcave penalized likelihood | VARIABLE SELECTION | ORACLE PROPERTIES | asymptotic normality | MODELS | LASSO | high-dimensionality | 62J07 | 62F12 | high dimensionality

Penalty function | Estimation bias | Dimensionality | Covariance | Threshing | Eigenvalues | Longitudinal data | Covariance matrices | Estimators | Perceptron convergence procedure | Covariance matrix | Nonconcave penalized likelihood | Asymptotic normality | High-dimensionality | Sparsistency | Consistency | REGRESSION | sparsistency | STATISTICS & PROBABILITY | LONGITUDINAL DATA | consistency | nonconcave penalized likelihood | VARIABLE SELECTION | ORACLE PROPERTIES | asymptotic normality | MODELS | LASSO | high-dimensionality | 62J07 | 62F12 | high dimensionality

Journal Article

Mathematics of Computation, ISSN 0025-5718, 10/2009, Volume 78, Issue 268, pp. 2127 - 2136

One of the key steps in compressed sensing is to solve the basis pursuit problem $\min _u \in \mathbb{R}^n \left\{ {\left\| u \right\|1:Au = f} \right\}$ ....

Algorithms | Mathematical theorems | Threshing | Iterative solutions | Signal noise | Signal processing | Mathematics | Research grants | Preprints | Perceptron convergence procedure

Algorithms | Mathematical theorems | Threshing | Iterative solutions | Signal noise | Signal processing | Mathematics | Research grants | Preprints | Perceptron convergence procedure

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2012, Volume 40, Issue 5, pp. 2389 - 2420

This paper considers estimation of sparse covariance matrices and establishes the optimal rate of convergence under a range of matrix operator norm and Bregman...

Minimax | Threshing | Eigenvalues | Mathematical vectors | Mathematics | Covariance matrices | Estimators | Uniformity | Estimation methods | Perceptron convergence procedure | Spectral norm | Bregman divergence | Optimal rate of convergence | Thresholding | Frobenius norm | Assouad's lemma | Covariance matrix estimation | Le Cam's method | Minimax lower bound | minimax lower bound | thresholding | spectral norm | optimal rate of convergence | STATISTICS & PROBABILITY | covariance matrix estimation | 62G09 | 62F12 | Le Cam’s method | Assouad’s lemma | 62H12

Minimax | Threshing | Eigenvalues | Mathematical vectors | Mathematics | Covariance matrices | Estimators | Uniformity | Estimation methods | Perceptron convergence procedure | Spectral norm | Bregman divergence | Optimal rate of convergence | Thresholding | Frobenius norm | Assouad's lemma | Covariance matrix estimation | Le Cam's method | Minimax lower bound | minimax lower bound | thresholding | spectral norm | optimal rate of convergence | STATISTICS & PROBABILITY | covariance matrix estimation | 62G09 | 62F12 | Le Cam’s method | Assouad’s lemma | 62H12

Journal Article

SIAM Journal on Numerical Analysis, ISSN 0036-1429, 1/2009, Volume 47, Issue 3, pp. 1760 - 1781

In this paper, we consider a variable-order fractional advection-diffusion equation with a nonlinear source term on a finite domain. Explicit and implicit...

Mathematical extrapolation | Error rates | Approximation | Partial differential equations | Numerical methods | Random walk | Differential equations | Boundary conditions | Perceptron convergence procedure | Method of lines | Extrapolation method | Fractional derivative of variable order | Nonlinear fractional advection-diffusion equation | Stability and convergence | Finite difference methods | stability and convergence | MATHEMATICS, APPLIED | APPROXIMATION | FELLER SEMIGROUPS | fractional derivative of variable order | nonlinear fractional advection-diffusion equation | finite difference methods | DISPERSION EQUATIONS | DIFFERENTIATION | OPERATORS | method of lines | extrapolation method | Advection-diffusion equation | Extrapolation | Numerical analysis | Stability | Mathematical analysis | Nonlinearity | Convergence

Mathematical extrapolation | Error rates | Approximation | Partial differential equations | Numerical methods | Random walk | Differential equations | Boundary conditions | Perceptron convergence procedure | Method of lines | Extrapolation method | Fractional derivative of variable order | Nonlinear fractional advection-diffusion equation | Stability and convergence | Finite difference methods | stability and convergence | MATHEMATICS, APPLIED | APPROXIMATION | FELLER SEMIGROUPS | fractional derivative of variable order | nonlinear fractional advection-diffusion equation | finite difference methods | DISPERSION EQUATIONS | DIFFERENTIATION | OPERATORS | method of lines | extrapolation method | Advection-diffusion equation | Extrapolation | Numerical analysis | Stability | Mathematical analysis | Nonlinearity | Convergence

Journal Article

Mathematics of Computation of the American Mathematical Society, ISSN 0025-5718, 01/2011, Volume 80, Issue 273, pp. 327 - 343

A semilocal convergence analysis for directional Newton methods in n

Nonlinear equations | Lipschitz condition | Mathematical induction | Computer analysis | Mathematical functions | Matrices | Mathematical surfaces | Newtons method | Perceptron convergence procedure | Systems of equations | Newton-Kantorovich-type hypothesis | Lipschitz/center-lipschitz condition | Directional Newton method | Lipschitz/center-Lipschitz condition | MATHEMATICS, APPLIED | systems of equations

Nonlinear equations | Lipschitz condition | Mathematical induction | Computer analysis | Mathematical functions | Matrices | Mathematical surfaces | Newtons method | Perceptron convergence procedure | Systems of equations | Newton-Kantorovich-type hypothesis | Lipschitz/center-lipschitz condition | Directional Newton method | Lipschitz/center-Lipschitz condition | MATHEMATICS, APPLIED | systems of equations

Journal Article

Filomat, ISSN 0354-5180, 1/2015, Volume 29, Issue 9, pp. 2069 - 2077

We introduce a concept of convergence of order 𝛼, with 0 < 𝛼 ≤ 1, with respect to a summability matrix method 𝐴 for sequences (which generalizes the notion...

Statistics | Perceptron convergence procedure

Statistics | Perceptron convergence procedure

Journal Article

SIAM Review, ISSN 0036-1445, 12/2011, Volume 53, Issue 4, pp. 747 - 772

We study the convergence speed of distributed iterative algorithms for the consensus and averaging problems, with emphasis on the latter. We first consider the...

Average speed | Algorithms | Automats | Eigenvalues | Markov chains | Matrices | Topology | Sensors | Conference proceedings | SIGEST | Perceptron convergence procedure | Cooper ative control | Consensus algorithms | Distributed averaging | MATHEMATICS, APPLIED | cooperative control | DYNAMICALLY CHANGING ENVIRONMENT | COVERAGE | consensus algorithms | MULTIAGENT SYSTEMS | DELAYS | OPTIMIZATION | NETWORKS | AGENTS | ALGORITHMS | distributed averaging | Technology application | Usage | Mobile communication systems | Wireless communication systems | Innovations | Control theory | Mathematical optimization | Iterative methods (Mathematics) | Methods | Studies | Averaging | Convergence

Average speed | Algorithms | Automats | Eigenvalues | Markov chains | Matrices | Topology | Sensors | Conference proceedings | SIGEST | Perceptron convergence procedure | Cooper ative control | Consensus algorithms | Distributed averaging | MATHEMATICS, APPLIED | cooperative control | DYNAMICALLY CHANGING ENVIRONMENT | COVERAGE | consensus algorithms | MULTIAGENT SYSTEMS | DELAYS | OPTIMIZATION | NETWORKS | AGENTS | ALGORITHMS | distributed averaging | Technology application | Usage | Mobile communication systems | Wireless communication systems | Innovations | Control theory | Mathematical optimization | Iterative methods (Mathematics) | Methods | Studies | Averaging | Convergence

Journal Article

The Annals of Statistics, ISSN 0090-5364, 12/2014, Volume 42, Issue 6, pp. 2164 - 2201

We provide theoretical analysis of the statistical and computational properties of penalized M-estimators that can be formulated as the solution to a possibly...

Objective functions | Statistical properties | Statistical theories | Least squares | Learning disabilities | Estimators | Perceptron convergence procedure | Logistics | Oracles | Computational statistics | Optimal statistical rate | Nonconvex regularized M-estimation | Geometric computational rate | Path-following method | REGRESSION | PATH | GRADIENT METHODS | geometric computational rate | ALGORITHM | STATISTICS & PROBABILITY | MULTISTAGE CONVEX RELAXATION | GENERALIZED LINEAR-MODELS | VARIABLE SELECTION | NONCONCAVE PENALIZED LIKELIHOOD | path-following method | optimal statistical rate | LASSO | REGULARIZATION | Statistics - Machine Learning | 90C52 | 62F30 | 90C26 | 62J12

Objective functions | Statistical properties | Statistical theories | Least squares | Learning disabilities | Estimators | Perceptron convergence procedure | Logistics | Oracles | Computational statistics | Optimal statistical rate | Nonconvex regularized M-estimation | Geometric computational rate | Path-following method | REGRESSION | PATH | GRADIENT METHODS | geometric computational rate | ALGORITHM | STATISTICS & PROBABILITY | MULTISTAGE CONVEX RELAXATION | GENERALIZED LINEAR-MODELS | VARIABLE SELECTION | NONCONCAVE PENALIZED LIKELIHOOD | path-following method | optimal statistical rate | LASSO | REGULARIZATION | Statistics - Machine Learning | 90C52 | 62F30 | 90C26 | 62J12

Journal Article

The Annals of Applied Probability, ISSN 1050-5164, 8/2012, Volume 22, Issue 4, pp. 1611 - 1641

On the one hand, the explicit Euler scheme fails to converge strongly to the exact solution of a stochastic differential equation (SDE) with a superlinearly...

Platens | Error rates | Approximation | Eulers method | Roots of functions | Numerical methods | Stochastic processes | Differential equations | Coefficients | Perceptron convergence procedure | Strong approximation | Nonglobally Lipschitz | Tamed Euler scheme | Implicit Euler scheme | Superlinearly growing coefficient | Backward Euler scheme | Euler scheme | Euler-Maruyama | Stochastic differential equation | nonglobally Lipschitz | STOCHASTIC DIFFERENTIAL-EQUATIONS | tamed Euler scheme | BEHAVIOR | strong approximation | UNIFORM APPROXIMATION | STATISTICS & PROBABILITY | IMPLICIT METHODS | superlinearly growing coefficient | SCHEME | stochastic differential equation | implicit Euler scheme | SYSTEMS | 65C30 | Euler–Maruyama

Platens | Error rates | Approximation | Eulers method | Roots of functions | Numerical methods | Stochastic processes | Differential equations | Coefficients | Perceptron convergence procedure | Strong approximation | Nonglobally Lipschitz | Tamed Euler scheme | Implicit Euler scheme | Superlinearly growing coefficient | Backward Euler scheme | Euler scheme | Euler-Maruyama | Stochastic differential equation | nonglobally Lipschitz | STOCHASTIC DIFFERENTIAL-EQUATIONS | tamed Euler scheme | BEHAVIOR | strong approximation | UNIFORM APPROXIMATION | STATISTICS & PROBABILITY | IMPLICIT METHODS | superlinearly growing coefficient | SCHEME | stochastic differential equation | implicit Euler scheme | SYSTEMS | 65C30 | Euler–Maruyama

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2012, Volume 40, Issue 5, pp. 2452 - 2482

Many statistical M-estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based...

Algorithms | Sample size | Linear regression | Statistical theories | Errors in statistics | Matrices | Mathematical vectors | Convexity | Estimators | Perceptron convergence procedure | High-dimensional inference | Convex optimization | Regularized Mestimation | SPARSITY | LINEAR-REGRESSION | DECOMPOSITION | STATISTICS & PROBABILITY | LOW-RANK MATRICES | VARIABLE SELECTION | PURSUIT | SHRINKAGE | LASSO | regularized M-estimation | convex optimization | COMPLETION | NOISY | 62H12 | 62F30

Algorithms | Sample size | Linear regression | Statistical theories | Errors in statistics | Matrices | Mathematical vectors | Convexity | Estimators | Perceptron convergence procedure | High-dimensional inference | Convex optimization | Regularized Mestimation | SPARSITY | LINEAR-REGRESSION | DECOMPOSITION | STATISTICS & PROBABILITY | LOW-RANK MATRICES | VARIABLE SELECTION | PURSUIT | SHRINKAGE | LASSO | regularized M-estimation | convex optimization | COMPLETION | NOISY | 62H12 | 62F30

Journal Article

Mathematics of Operations Research, ISSN 0364-765X, 5/2010, Volume 35, Issue 2, pp. 438 - 457

We study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L(x, y) = f(x) + Q(x, v)...

Mathematical manifolds | Algorithms | Mathematical theorems | Critical points | Mathematical constants | Mathematical functions | Mathematical inequalities | Mathematics | Convexity | Perceptron convergence procedure | Tame optimization | Finite | Kurdyka-Lojasiewicz inequality | Nonconvex optimization | O-minimal structures | Proximal algorithms | Alternating minimization algorithms | Convergence rate | Alternating projections algorithms | MATHEMATICS, APPLIED | proximal algorithms | o-minimal structures | finite convergence time | FEASIBILITY | alternating projections algorithms | SIGNAL RECOVERY | gradient systems | nonconvex optimization | tame optimization | sparse reconstruction | ALGORITHMS | alternating minimization algorithms | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE | convergence rate | GEOMETRY | Usage | Equality | Mathematical optimization | Analysis

Mathematical manifolds | Algorithms | Mathematical theorems | Critical points | Mathematical constants | Mathematical functions | Mathematical inequalities | Mathematics | Convexity | Perceptron convergence procedure | Tame optimization | Finite | Kurdyka-Lojasiewicz inequality | Nonconvex optimization | O-minimal structures | Proximal algorithms | Alternating minimization algorithms | Convergence rate | Alternating projections algorithms | MATHEMATICS, APPLIED | proximal algorithms | o-minimal structures | finite convergence time | FEASIBILITY | alternating projections algorithms | SIGNAL RECOVERY | gradient systems | nonconvex optimization | tame optimization | sparse reconstruction | ALGORITHMS | alternating minimization algorithms | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE | convergence rate | GEOMETRY | Usage | Equality | Mathematical optimization | Analysis

Journal Article

Taiwanese Journal of Mathematics, ISSN 1027-5487, 12/2010, Volume 14, Issue 6, pp. 2497 - 2511

In this paper, we first consider a broad class of nonlinear mappings containing the classes of nonexpansive mappings, nonspreading mappings, and hybrid...

Ergodic theory | Mathematical theorems | Real numbers | Applied mathematics | Diagonal lemma | Hilbert spaces | Banach space | Perceptron convergence procedure | Mean convergence | Hilbert space | Nonspreading mapping | Hybrid mapping | Nonexpansive mapping | Fixed point | MATHEMATICS | NONLINEAR MAPPINGS | NONEXPANSIVE-MAPPINGS | OPERATORS

Ergodic theory | Mathematical theorems | Real numbers | Applied mathematics | Diagonal lemma | Hilbert spaces | Banach space | Perceptron convergence procedure | Mean convergence | Hilbert space | Nonspreading mapping | Hybrid mapping | Nonexpansive mapping | Fixed point | MATHEMATICS | NONLINEAR MAPPINGS | NONEXPANSIVE-MAPPINGS | OPERATORS

Journal Article

Econometric Theory, ISSN 0266-4666, 6/2008, Volume 24, Issue 3, pp. 726 - 748

This paper presents a set of rate of uniform consistency results for kernel estimators of density functions and regressions functions. We generalize the...

Density estimation | Kernel functions | Wands | Linear regression | Time series | Polynomials | Mathematical functions | Estimators | Consistent estimators | Perceptron convergence procedure | WEAK | NONPARAMETRIC REGRESSION | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | MODELS | DENSITY-ESTIMATION | Studies | Econometrics

Density estimation | Kernel functions | Wands | Linear regression | Time series | Polynomials | Mathematical functions | Estimators | Consistent estimators | Perceptron convergence procedure | WEAK | NONPARAMETRIC REGRESSION | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | MODELS | DENSITY-ESTIMATION | Studies | Econometrics

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2005, Volume 33, Issue 4, pp. 1538 - 1579

Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version,...

Error rates | Logistic regression | Mathematical procedures | Statistical theories | Machine learning | Least squares | Mathematical functions | Estimators | Consistent estimators | Perceptron convergence procedure | Greedy optimization | Boosting | Early stopping | Consistency | Matching pursuit | ADABOOST | boosting | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | NETWORKS | matching pursuit | VOTING METHODS | ALGORITHMS | consistency | CLASSIFIERS | EXPLANATION | CLASSIFICATION METHODS | MARGIN | GREEDY APPROXIMATION | greedy optimization | early stopping | 62G05 | 62G08

Error rates | Logistic regression | Mathematical procedures | Statistical theories | Machine learning | Least squares | Mathematical functions | Estimators | Consistent estimators | Perceptron convergence procedure | Greedy optimization | Boosting | Early stopping | Consistency | Matching pursuit | ADABOOST | boosting | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | NETWORKS | matching pursuit | VOTING METHODS | ALGORITHMS | consistency | CLASSIFIERS | EXPLANATION | CLASSIFICATION METHODS | MARGIN | GREEDY APPROXIMATION | greedy optimization | early stopping | 62G05 | 62G08

Journal Article

Journal of Computational and Graphical Statistics, ISSN 1061-8600, 12/1998, Volume 7, Issue 4, pp. 434 - 455

We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances...

Convergence diagnosis | Inference | Markov chain Monte Carlo | Statistical discrepancies | Musical intervals | Markov chains | Scalars | Unbiased estimators | Parametric models | Interval estimators | Covariance matrices | Perceptron convergence procedure | Markov chain monte carlo | inference | MODELS | STATISTICS & PROBABILITY | convergence diagnosis | CHAIN MONTE-CARLO | POSTERIOR DISTRIBUTIONS

Convergence diagnosis | Inference | Markov chain Monte Carlo | Statistical discrepancies | Musical intervals | Markov chains | Scalars | Unbiased estimators | Parametric models | Interval estimators | Covariance matrices | Perceptron convergence procedure | Markov chain monte carlo | inference | MODELS | STATISTICS & PROBABILITY | convergence diagnosis | CHAIN MONTE-CARLO | POSTERIOR DISTRIBUTIONS

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2011, Volume 39, Issue 4, pp. 1878 - 1915

Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing...

Community structure | Algorithms | Spectral theory | Eigenvalues | Eigenvectors | Laplacians | Matrices | Stochastic models | Spectral graph theory | Perceptron convergence procedure | Latent space model | Principal components analysis | Convergence of eigenvectors | Spectral clustering | Stochastic Blockmodel | Clustering | ALGORITHM | STATISTICS & PROBABILITY | MODEL | latent space model | principal components analysis | PREDICTION | CONSISTENCY | SOCIAL NETWORK ANALYSIS | COMMUNITY STRUCTURE | DIRECTED-GRAPHS | EIGENVECTORS | convergence of eigenvectors | clustering | 62H30 | 60B20 | 62H25

Community structure | Algorithms | Spectral theory | Eigenvalues | Eigenvectors | Laplacians | Matrices | Stochastic models | Spectral graph theory | Perceptron convergence procedure | Latent space model | Principal components analysis | Convergence of eigenvectors | Spectral clustering | Stochastic Blockmodel | Clustering | ALGORITHM | STATISTICS & PROBABILITY | MODEL | latent space model | principal components analysis | PREDICTION | CONSISTENCY | SOCIAL NETWORK ANALYSIS | COMMUNITY STRUCTURE | DIRECTED-GRAPHS | EIGENVECTORS | convergence of eigenvectors | clustering | 62H30 | 60B20 | 62H25

Journal Article

The Rocky Mountain Journal of Mathematics, ISSN 0035-7596, 1/2006, Volume 36, Issue 3, pp. 799 - 809

We consider two classes of q-continued fraction whose odd and even parts are limit 1-periodic for |q| > 1, and give theorems which guarantee the convergence of...

Integers | Mathematical theorems | Fractions | Continued fractions | Perceptron convergence procedure | Rogers-Ramanujan | MATHEMATICS | continued fractions | Mathematics - Number Theory | 11A55 | 40A15

Integers | Mathematical theorems | Fractions | Continued fractions | Perceptron convergence procedure | Rogers-Ramanujan | MATHEMATICS | continued fractions | Mathematics - Number Theory | 11A55 | 40A15

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2007, Volume 35, Issue 1, pp. 192 - 223

We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor...

Minimax | Density estimation | Spectral energy distribution | Time series | White noise | Markov chains | Entropy | Polynomials | Asymptotics | Density | Perceptron convergence procedure | Covering numbers | Hellinger distance | Independent nonidentically distributed observations | Infinite dimensional model | Rate of convergence | Posterior distribution | Tests | NONPARAMETRIC PROBLEMS | MAXIMUM-LIKELIHOOD | DENSITY-ESTIMATION | rate of convergence | STATISTICS & PROBABILITY | independent nonidentically distributed observations | CONSISTENCY | infinite dimensional model | tests | MODELS | covering numbers | posterior distribution | TIME-SERIES | BERNSTEIN POLYNOMIALS | 62G08 | 62G20

Minimax | Density estimation | Spectral energy distribution | Time series | White noise | Markov chains | Entropy | Polynomials | Asymptotics | Density | Perceptron convergence procedure | Covering numbers | Hellinger distance | Independent nonidentically distributed observations | Infinite dimensional model | Rate of convergence | Posterior distribution | Tests | NONPARAMETRIC PROBLEMS | MAXIMUM-LIKELIHOOD | DENSITY-ESTIMATION | rate of convergence | STATISTICS & PROBABILITY | independent nonidentically distributed observations | CONSISTENCY | infinite dimensional model | tests | MODELS | covering numbers | posterior distribution | TIME-SERIES | BERNSTEIN POLYNOMIALS | 62G08 | 62G20

Journal Article

Mathematics of Computation of the American Mathematical Society, ISSN 0025-5718, 01/2007, Volume 76, Issue 257, pp. 287 - 298

For the non-Hermitian and positive semidefinite systems of linear equations, we derive necessary and sufficient conditions for guaranteeing the unconditional...

Linear systems | Economic theory | Iterative solutions | Eigenvalues | Matrices | Eigenvectors | Mathematics | Linear equations | Coefficients | Perceptron convergence procedure | Positive semidefinite matrix | Hermitian and skew-Hermitian splitting | Splitting iteration method | Convergence | Non-Hermitian matrix

Linear systems | Economic theory | Iterative solutions | Eigenvalues | Matrices | Eigenvectors | Mathematics | Linear equations | Coefficients | Perceptron convergence procedure | Positive semidefinite matrix | Hermitian and skew-Hermitian splitting | Splitting iteration method | Convergence | Non-Hermitian matrix

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2014, Volume 42, Issue 3, pp. 1131 - 1144

The optimal rate of convergence of estimators of the integrated volatility, for a discontinuous Itô semimartingale sampled at regularly spaced times and over a...

Brownian motion | Minimax | Space based observatories | Eigenfunctions | Mathematical functions | Interval estimators | Estimators | Martingales | Perceptron convergence procedure | Infinite activity | High frequency | Jumps | Volatility | Semimartingale | Discrete sampling | infinite activity | discrete sampling | high frequency | jumps | STATISTICS & PROBABILITY | volatility | 60H99 | 60J75 | 62C20 | 62G20 | 62M09

Brownian motion | Minimax | Space based observatories | Eigenfunctions | Mathematical functions | Interval estimators | Estimators | Martingales | Perceptron convergence procedure | Infinite activity | High frequency | Jumps | Volatility | Semimartingale | Discrete sampling | infinite activity | discrete sampling | high frequency | jumps | STATISTICS & PROBABILITY | volatility | 60H99 | 60J75 | 62C20 | 62G20 | 62M09

Journal Article

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