Regression and Log-Linear Models: Parametric
Testing parametric models in linear-directional regression
... and linear variables at the same time, the joint behavior could be modeled by considering a flexible density estimator (Garc´ıa-Portugu´ es et ...a regression approach may be more useful, allowing at the ...
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Testing parametric models in linear-directional regression
... for parametric regression models with scalar response and directional predictor, that is, vectors in a sphere of arbitrary ...a parametric regression estimator, where the smooth ...
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Comparison of Parametric (OLS) and Non-Parametric (THEIL’S) Linear Regression
... of parametric and non-parametric linear ...squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling ...Theil’s regression. It is ...
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Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression
... about, models built with pool level models cannot distinguish the underlying ...powerful parametric specifications. Specifically, non- parametric kernel regression is employed to ...
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Tests for independence in non-parametric heteroscedastic regression models
... j = 1 e it e ˆ j , corresponding to X and e, respectively, is employed. Note that in the multivariate i.i.d. setting considered by Meintanis and Iliopoulos [ 9 ], independence procedures based on the ECF often outperform ...
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Bayesian influence diagnostic methods for parametric regression models
... survival models for both continuous survival time data and grouped survival ...Bayesian regression models. These models include linear models, mixed models, generalized ...
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Parametric Regression Models Using Reversed Hazard Rates
... sion models are useful in such contexts to assess the effect of covariates on ...These models can be for- mulated in many ways and several types are in common ...use. Parametric regression ...
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Statistical inference on linear and partly linear regression with spatial dependence: parametric and nonparametric approaches
... tivariate linear regression model with the disturbances following a multivariate SAR ...The parametric set-up of the SAR model allows us to employ likelihood based ...the regression is ...
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A test for the parametric form of the variance function in apartial linear regression model
... email: [email protected] August 13, 2007 Abstract We consider the problem of testing for a parametric form of the variance function in a partial linear regression model. A new ...
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Log-Linear Models a.k.a. Logistic Regression, Maximum Entropy Models
... § Sequences of words, tags, morphemes, phonemes ( n-grams, FSAs, FSTs ; regex compilation, best-paths , forward-backward , collocations ).. § Vectors ( clusters ).[r] ...
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Some Recent Developments in Parametric and Nonparametric Regression Models.
... Despite several existing nonparametric quantile function estimators, selecting relevant predictors in multi-dimensional data is an important yet challenging topic that has not been addressed in depth. Variable selection ...
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Fitting Flexible Parametric Regression Models with GLDreg in R
... GλD regression models was illustrated; rather than confining the regression model to only examining the mean or median as is the case of linear regression or classic quantile ...
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Instrumental Regression in Partially Linear Models
... the parametric variables X are endogeneous, treatment of the endogeneity by instrumental variables is analogous to the idea of GMM estimators, although the presence of the nonparametric exogenous part φ(Z) leads ...
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Variable Selection Procedures In Linear Regression Models
... There are 384 women older than 50 at the study entry and 2304 observations in total. We assume that the random effects v i ∼ N(0, σ u 2 ). In order the employ variable selection, we introduce some dummy variables, ...
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Bootstrap Tests of Nonnested Linear Regression Models
... As we discuss in Davidson and MacKinnon (1997a), the finite-sample perfor- mance of a bootstrap test at level α under the null hypothesis depends, to leading order, on the bias of the bootstrap estimate of the size-α ...
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Finite Mixtures of Generalized Linear Regression Models
... zero-inflated models (B¨ohning et al, ...same parametric family ...intercept models where only the intercept follows a finite mixture distribution while all other regression coefficients are ...
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Semi-Parametric Mixture Models Through Log-Concave Density Estimation
... the regression context. For the regression model y = x T β + , in order to incorporate the possible outliers and get a robust regression estimate of β, we can model the density by the proposed ...
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Inferential Models for Linear Regression
... 7. Discussion In this paper we have elaborated on the recently proposed inferential model framework, which produces prior-free probabilistic summaries of evidence for/against assertions of interest and, moreover, these ...
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Log Linear Models for Word Alignment
... (3) Typically, the source language sentence e and the target sentence f are the fundamental knowledge sources for the task of finding word alignments. Lin- guistic data, which can be used to identify associ- ations ...
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Evaluation of regression methods for log-normal data - linear models for environmental exposure and biomarker outcomes
... Inom miljömedicinsk forskning är det vanligt att man är intresserad av sambanden mellan olika variabler. Vid till exempel bedömning av yrkesexponering är man ofta intresserad av sambanden mellan exponeringen för ett ...
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