Using models for the error
Error Correction Using Probabilistic Language Models
... Distribution Models A topic-sensitive distribution model can overcome the above problems by building the frequency model specific to a ...the models are built individually for each of the topic. To correct ...
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Error Resilient Video Compression Using Behavior Models
... an error prone ...the models (25) and the parameters we have obtained in Section 4 for the first ten frames of ...our models to optimize the settings for a whole ...our models and with two ...
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Testing Multiplicative Error Models Using Conditional Moment Tests
... We have proposed a robust form of Newey’s (1985) conditional moment test for functional misspecification in multiplicative error models. The proposed test is robust to any potential misspecification other ...
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Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data
... [6] Sepanski, J. and Lee, L.F. (1995) Estimation of Linear and Nonlinear Er- rors-in-Variables Models Using Validation Data. Journal of the American Statistical Association , 90, 130-140. ...
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Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models
... Keywords: Multiplicative error models, volatility, liquidity, high-frequency data. JEL Classification: C13, C32, C52 1 Introduction Due to the permanently increasing availability of high-frequency financial ...
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Demonstrating The Use Of Vector Error Correction Models Using Simulated Data
... Vector Error Correction Models INTRODUCTION egressions between levels of variables may have high covariation because of persistence in the base levels of the variables rather than persistence in the changes ...
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Statistical Methods for Nonlinear Dynamic Models with Measurement Error Using the Ricker Model
... measurement error and find that the parameter is overestimated no matter its true ...dynamic models is complex due to the convoluted nature of the unconditional expected values ...measurement error ...
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Taking error into account when fitting models using approximate Bayesian computation
... Abstract (maximum 200 words) Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult ...
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Error analysis of dynamical models in epidemiology
... compare various simulation models from the point of accuracy and conduct the error analysis.. The integral equation for x(t) is implicit, as shown by.[r] ...
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Saddlepoint test in measurement error models
... measurement error models for small or moderate sample sizes, where the classical first order asymp- totic analysis often fails to provide accurate ...functional models no distri- butional assumptions ...
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Fitting Data with Different Error Models »
... the error model determines the parameter values, since we should always minimize the least squares of the ...different error models, which can be applied to fitting a line in a least-squares ...The ...
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On the Approximation Error of Mean-Field Models
... field models instead of diffusion ...approximation error, the paper identi- fies a fundamental connection between the perturbation the- ory for nonlinear systems and the convergence of mean- field ...the ...
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Estimation of nonlinear error correction models
... the error correction term appears in a bounded transition function ...smooth models assuming that the cointegrating vector estimators converge at a certain ...smooth models while the threshold ...
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Auxiliary Objectives for Neural Error Detection Models
... assign error labels to tokens based on the manual annotation available in the training ...learning error patterns. In or- der to train models that generalise well with lim- ited training examples, we ...
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Forecasting Performance of Alternative Error Correction Models
... countries using same lags for each ...VECM models the loading coefficients was insignificant only in ...lags using Schwarz criteria using ARDL method are four for Korea, Philippines, Pakistan ...
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Sequential regression measurement error models with application
... regression models using the covariates revealed by the end of the current stage, but this is only practical if the number of covariates is not too ...measurement error. In this article, we propose a ...
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Testing for Spatial Error Dependence in Probit Models
... Abstract In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the formal differences between the ...
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On calculating estimates of stratified error-components models
... Abstract This note provides an AECM (alternating expectation conditional maximization) algorithm for calculating maximum-likelihood estimates of stratified error-components models. An advantage it has over ...
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Vector Autoregression and Vector Error-Correction Models
... cointegrated models can be estimated either directly or in two ...the error-correction model, or we can estimate the α coeffi- cients jointly with the β coefficients on the ...consider models with ...
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Robustness for regression models with asymmetric error distribution
... regression models with asymmetrical error distribution, more precisely, with extreme value error ...regression models are applied in a variety of different application domains, ...asymmetric ...
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