Results 211 to 220 of about 12,224 (267)

Change Point Detection in Panel Linear Regression Models Based on Jump Information Criterion. [PDF]

Entropy (Basel)
Zhao W, Fan L, Xia Z.
europepmc   +1 more source

Replicable Bandits for Digital Health Interventions. [PDF]

Stat Sci
Zhang KW, Closser N, Trella AL, Murphy SA.   +3 more
europepmc   +1 more source

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Least squares estimates and the coverage of least squares costs

52nd IEEE Conference on Decision and Control, 2013
The least squares estimate xN minimizes the sum of the squared residuals equation over a finite set of observations (Ai, bi). At x = xN, the squared residuals ∥AixN-bi∥2 are called the “empirical costs”. Intuitively, the empirical costs carry information on the probability distribution of the cost ∥AxN-b∥2 that is paid for other, yet unseen, values of (
Carè, Algo, Garatti, Simone, Campi, Marco C.   +2 more
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Asymmetric Least Squares Estimation and Testing

Econometrica, 1987
This paper considers estimation and hypothesis tests for coefficients of linear regression models, where the coefficient estimates are based on location measures defined by an asymmetric least squares criterion function. These asymmetric least squares estimators have properties which are analogous to regression quantile estimators, but are much simpler
Newey, Whitney K, Powell, James L
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Fastiterative methods for least squares estimations

Numerical Algorithms, 1994
The paper is a continuation of the research works of the authors in connection with solving of Toeplitz systems (or Yule-Walker type systems) by the preconditioned conjugate gradient method using circulant preconditioners. The authors propose some circulant preconditioners with the aid of the spectral density function of the given discrete-time ...
Michael K. Ng 0001, Raymond H. Chan
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Least squares parameter estimation

Automatica, 1979
Abstract This article demonstrates the application of least squares for the estimation of system parameters. Analytic as well as numerical approaches are described. The model of the system dynamics is assumed in the form of regression model. Solutions are discussed for the case of white noise and correlated noise corrupting the useful output signal ...
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Minimax Estimators Dominating the Least-Squares Estimator

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006
We present several analytical and numerical results demonstrating the superiority of minimax estimators over least-squares (LS) estimation. We show that, for any bounded parameter set, a linear minimax estimator achieves lower mean-squared error than the LS estimator, over the entire parameter set.
Zvika Ben-Haim, Yonina C. Eldar
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