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Displaying similar documents to “Recursive estimates of quantile based on 0-1 observations”

Fitting a linear regression model by combining least squares and least absolute value estimation.

Sira Allende, Carlos Bouza, Isidro Romero (1995)

Qüestiió

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Robust estimation of the multiple regression is modeled by using a convex combination of Least Squares and Least Absolute Value criterions. A Bicriterion Parametric algorithm is developed for computing the corresponding estimates. The proposed procedure should be specially useful when outliers are expected. Its behavior is analyzed using some examples.

Parameter estimation of S-distributions with alternating regression.

I-Chun Chou, Harald Martens, Eberhard O. Voit (2007)

SORT

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We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its enormous...

Coupling a stochastic approximation version of EM with an MCMC procedure

Estelle Kuhn, Marc Lavielle (2004)

ESAIM: Probability and Statistics

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The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with markovian perturbations are used...

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

Grigorova, Denitsa, Encheva, Elitsa, Gueorguieva, Ralitza (2013)

Serdica Journal of Computing

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The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in...