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Robust Logistic Regression using Shift Parameters
Robust Logistic Regression Shift Parameters
2013/6/17
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that aut...
Estimation in Systems of Ordinary Differential Equations Linear in the Parameters
local polynomials Lotka-Volterra nonparamet-ric regression ordinary differential equation plug-in estimators
2013/6/14
Many phenomena in biology, chemistry, physics, and engineering are modeled by a system of possibly nonlinear ordinary differential equations that are linear in their unknown constants. Current methods...
Propagation of initial errors on the parameters for linear and Gaussian state space models
Kalman filter Extended Kalman filter State space mod-els Autoregressive process
2013/4/27
For linear and Gaussian state space models parametrized by $\theta_0 \in \Theta \subset \R^{r}, r \geq 1$ corresponding to the vector of parameters of the model, the Kalman filter gives exactly the so...
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
Nuisance Parameters Post-Model-Selection Random Critical Values
2012/11/22
We point out that the ideas underlying some test procedures recently proposed for testing post-model-selection (and for some other test problems) in the econometrics literature have been around for qu...
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
Consistent selection of tuning parameters via variable selection stability
kappa coefficient penalized regression selection consistency stability tuning
2012/9/17
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
Demmartingales and the functionnal Hill process for small parameters
Associated random variables demimartingales asymptotic laws func-tional Hill process extreme value theory statistical tests.
2012/9/17
Association of random variables and Demimartingales are recent elds for handling asymptotic behaviors of sums of dependent random vari-ables. We apply their techniques to establish the asymptotic law...
Estimation of Scale and Hurst Parameters of Semi-Selfsimilar Processes
Hurst estimation Discrete self-similarity Fractional Brownian motion Semi-selfsimilar processes Scale parameter.
2012/9/19
The characteristic feature of semi-selfsimilar process is the invariance of its finite dimensional distributions by certain dilation for specific scaling factor. Estimating the scale parameter λand th...
Moment based estimation of stochastic Kronecker graph parameters
Moment based estimation stochastic Kronecker graph
2011/7/5
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters.
Marginal log-linear parameters for graphical Markov models
multivariate discrete statistical models parametrization marginal log-linear graphical Markov models
2011/6/20
The parametrization of multivariate discrete statistical models by marginal log-linear
(MLL) parameters provides a great deal of flexibility; in particular, different MLL parametrizations
under line...
Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters
Bayesian model selection growing number of parameters Posterior model consistency consistency of Bayes factor consistency of posterior odds ratio Gibbs sampling
2011/3/18
Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which...
Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model
parameter identifiability differential algebra hybrid optimization
2010/10/19
Modeling viral dynamics in HIV/AIDS studies has resulted in a deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral ...
Beta-binomial/gamma-Poisson regression models for repeated counts with random parameters
bivariate counts longitudinal data overdispersion random effects regressionmodels
2010/3/11
Beta-binomial/Poisson models have been used by many authors to model multivariate
count data. Lora and Singer (Statistics in Medicine, 2008) extended such models
to accommodate repeated multivariate...
An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors
ordered explanatory variable constrained estimation least squares logistic regression Coxregression active set algorithm
2010/3/18
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariate...
Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations
Ordinary differential equations parameters estimation profiling procedure consistency asymptotic normality
2010/3/18
Ordinary differential equations (ODEs) are commonly used to
model dynamic behavior of a system. Because many parameters are
unknown and have to be estimated from the observed data, there
is growing...