The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Antonio Diez De Los Rios - One of the best experts on this subject based on the ideXlab platform.

  • a new Linear Estimator for gaussian dynamic term structure models
    Journal of Business & Economic Statistics, 2015
    Co-Authors: Antonio Diez De Los Rios
    Abstract:

    This article proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models. This new Estimator is an asymptotic least-square Estimator defined by the no-arbitrage conditions upon which these models are built. Further, we note that our Estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches might lose their tractability. We provide an empirical application in the context of the Canadian bond market.

  • a new Linear Estimator for gaussian dynamic term structure models
    Social Science Research Network, 2014
    Co-Authors: Antonio Diez De Los Rios
    Abstract:

    This paper proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models that avoids numerical optimization. This new Estimator is an asymptotic least squares Estimator defi ned by the no-arbitrage conditions upon which these models are built. Further, we note that our Estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches might lose their tractability. We provide an empirical application in the context of the Canadian bond market.

  • a new Linear Estimator for gaussian dynamic term structure models
    Research Papers in Economics, 2013
    Co-Authors: Antonio Diez De Los Rios
    Abstract:

    This paper proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models that avoids numerical optimization. This new Estimator is an asymptotic least squares Estimator defined by the no-arbitrage conditions upon which these models are built. We discuss some efficiency considerations of this Estimator, and show that it is asymptotically equivalent to maximum likelihood estimation. Further, we note that our Estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches lose their tractability. We provide an empirical application in the context of the Canadian bond market.

Larry Wasserman - One of the best experts on this subject based on the ideXlab platform.

  • Rodeo: Sparse Nonparametric Regression in High Dimensions
    2018
    Co-Authors: John Lafferty, Larry Wasserman
    Abstract:

    We present a method for simultaneously performing bandwidth selection and variable selection in nonparametric regression. The method starts with a local Linear Estimator with large bandwidths, and incrementally decreases the bandwidth in directions where the gradient of the Estimator with respect to bandwidth is large. When the unknown function satisfies a sparsity condition, the approach avoids the curse of dimensionality. The method - called rodeo (regularization of derivative expectation operator) - conducts a sequence of hypothesis tests, and is easy to implement. A modified version that replaces testing with soft thresholding may be viewed as solving a sequence of lasso problems. When applied in one dimension, the rodeo yields a method for choosing the locally optimal bandwidth.

  • rodeo sparse greedy nonparametric regression
    Annals of Statistics, 2008
    Co-Authors: John Lafferty, Larry Wasserman
    Abstract:

    We present a greedy method for simultaneously performing local bandwidth selection and variable selection in nonparametric regression. The method starts with a local Linear Estimator with large bandwidths, and incrementally decreases the bandwidth of variables for which the gradient of the Estimator with respect to bandwidth is large. The method¯called rodeo (regularization of derivative expectation operator)¯conducts a sequence of hypothesis tests to threshold derivatives, and is easy to implement. Under certain assumptions on the regression function and sampling density, it is shown that the rodeo applied to local Linear smoothing avoids the curse of dimensionality, achieving near optimal minimax rates of convergence in the number of relevant variables, as if these variables were isolated in advance.

Wei Liu - One of the best experts on this subject based on the ideXlab platform.

  • convergence of optimal Linear Estimator with multiplicative and time correlated additive measurement noises
    IEEE Transactions on Automatic Control, 2019
    Co-Authors: Wei Liu, Peng Shi
    Abstract:

    In this paper, the problem of convergence for the optimal Linear Estimator of discrete-time Linear systems with multiplicative and time-correlated additive measurement noises is studied. By defining a new random vector that consists of the innovation, error, and part of the noise in the new measurement obtained from the measurement differencing method, we obtain convergence conditions of the optimal Linear Estimator by equivalently studying the convergence of the expectation of a random matrix where the random matrix is the product of the new vector and its transpose. It is also shown that the state error covariance matrix of the optimal Linear Estimator converges to a unique fixed point under appropriate conditions and, moreover, this fixed point can be obtained by solving a set of matrix equations.

  • optimal estimation for discrete time Linear systems in the presence of multiplicative and time correlated additive measurement noises
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Wei Liu
    Abstract:

    In this paper, the state estimation problem for discrete-time Linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a Linear system model with white noise. An optimal Linear Estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix. The proposed Estimator has a recursive structure, and has time-independent computation and storage load. Computer simulations are carried out to demonstrate the performance of the proposed Estimator. The simulation results show the superiority of the proposed Estimator.

John Lafferty - One of the best experts on this subject based on the ideXlab platform.

  • Rodeo: Sparse Nonparametric Regression in High Dimensions
    2018
    Co-Authors: John Lafferty, Larry Wasserman
    Abstract:

    We present a method for simultaneously performing bandwidth selection and variable selection in nonparametric regression. The method starts with a local Linear Estimator with large bandwidths, and incrementally decreases the bandwidth in directions where the gradient of the Estimator with respect to bandwidth is large. When the unknown function satisfies a sparsity condition, the approach avoids the curse of dimensionality. The method - called rodeo (regularization of derivative expectation operator) - conducts a sequence of hypothesis tests, and is easy to implement. A modified version that replaces testing with soft thresholding may be viewed as solving a sequence of lasso problems. When applied in one dimension, the rodeo yields a method for choosing the locally optimal bandwidth.

  • rodeo sparse greedy nonparametric regression
    Annals of Statistics, 2008
    Co-Authors: John Lafferty, Larry Wasserman
    Abstract:

    We present a greedy method for simultaneously performing local bandwidth selection and variable selection in nonparametric regression. The method starts with a local Linear Estimator with large bandwidths, and incrementally decreases the bandwidth of variables for which the gradient of the Estimator with respect to bandwidth is large. The method¯called rodeo (regularization of derivative expectation operator)¯conducts a sequence of hypothesis tests to threshold derivatives, and is easy to implement. Under certain assumptions on the regression function and sampling density, it is shown that the rodeo applied to local Linear smoothing avoids the curse of dimensionality, achieving near optimal minimax rates of convergence in the number of relevant variables, as if these variables were isolated in advance.

Y T Chan - One of the best experts on this subject based on the ideXlab platform.

  • an unbiased Estimator for bearings only tracking and doppler bearing tracking
    International Conference on Acoustics Speech and Signal Processing, 2003
    Co-Authors: Y T Chan
    Abstract:

    The objective of both bearings-only tracking (BOT) and Doppler-bearing tracking (DBT) is to obtain the target trajectory based on bearings, and Doppler and bearing measurements respectively, from an observer to the target. The BOT and DBT problems are nontrivial because the measurement equations are nonLinear. The pseudo Linear formulation allows a Linear Estimator to solve for the solution, but the solution obtained is biased. This paper proposes an Estimator based on the pseudo Linear equations that produce an unbiased solution. The proposed method applies least-squares minimization on the pseudo Linear equations with appropriate constraints on the unknown parameters. Simulations are included to illustrate the performance of the proposed Estimator. The proposed Estimator achieves the Cramer-Rao lower bound (CRLB) for Gaussian noise around small error region.