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Showing 1 to 16 of 16 for “"Regression functions"”.
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Semiparametric mixture models
… propose a new class of semiparametric mixture of regression models, where the mixing proportions and variances are constants, but the component regression functions are smooth functions of a covariate. A one-step backfitting estimate and two EM-type algorithms have been proposed to achieve the …
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Quantile-based methods for prediction, risk measurement and inference
… simultaneous confidence bands for quantile regression functions when the predictor variables are constrained within a region is considered. In this context, a method is introduced that makes use of the asymmetric Laplace errors in conjunction with a simulation based algorithm to create …
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On robust jump detection in regression surfaces with applications to image analysis
… can be used to detect discontinuities in regression functions. In smooth regions, an estimator using only observations on the left side will be similar to the estimator using only observations on the right side. In contrast, near jump points, the difference of these two estimates will be …
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Optimal Simultaneous Confidence Bounds in Regression
… simultaneous confidence bounds for multilinear regression functions with intercept, over bounded regions, is considered. Conditions are derived which imply that the Scheffe-type bound beats the constant width bound for the case of one-sided or two-sided bounding, in the sense of having smaller …
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On robust corner-preserving smoothing in image processing
… M-kernel estimators in one- and two-dimensional regression. The M-kernel smoother was first introduced by Härdle and Gasser (1984) who provide a restricted valid proof of consistency for a monotone score function and show the minimax property. Chu et al. (1998) take a density as (redescending) …
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Estimating Pedestrian Crashes at Urban Signalized Intersections
… count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle …
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Low birthweight discrimination using maternal variables.
… volume in particular), and aims to result functions which can make reasonably accurate identification of women who are likely to have small for gestational age infants, based on known maternal factors. By the method of discriminant analysis and multiple regression, functions have been …
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Modeling temporally-regulated effects on distributions
… are not suited for this task, we introduce a new regression model for (ordinal-value , univariate-distribution) covariate-response pairs where the class of regression-functions reflects coherent changes to the distributions over increasing levels of the covariate, a concept we refer to as trends …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
… to be clustered together. Multiple logistic regression functions generate a set of sticks with each dominating a spatially localized segment. The proposed model is employed on image segmentation and speaker diarization, yielding generally homogeneous segments with sharp boundaries.</p> <p>In …
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Statistical methods for weather-related insurance claims
… non-linear effects, the class of monotonic regression functions is considered. Specifically, this work is the first to de- fine flexible dependence structures for such functions. In particular, the first approach considers a Bayesian framework and estimates are obtained by Markov chain Monte …
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Investigating Acoustic Properties of Biocomposite from Waste Streams
… was analyzed and modeled mathematically. Using regression functions that combined distribution curves and polynomials to represent the acoustic absorption coefficients frequency profiles for the studied biocomposites was completed. Also, for each sample set, an approximation to the noise …
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Modern Methods for Variable Significance Testing
… that the data are jointly Gaussian. A novel regression-based test, called the Generalised Hilbertian Covariance Measure (GHCM), is presented and theoretical guarantees for uniform asymptotic Type I error control are provided with the key assumption requiring that the product of the mean …
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Symmetry and Other Structures: Topics in Nonparametric Regression
In regression we measure covariate samples X_i and response variables Y_i and seek to estimate the conditional mean, or regression function, f(x) = E(Y_i ∣ X_i = x) within some hypothesis class F of possible regression functions. Estimation of a nonparametric regression function f ∶ [0, 1]^d → R …
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Uncertainty-Integrated Surrogate Modeling for Complex System Optimization
… of the error distributions are represented as functions of the density of intermediate training points through nonlinear regression, assuming a smooth decreasing trend of errors with increasing sample density. These regression functions are then used to predict the expected median and maximum …
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Análisis e interpretación de emisiones acústicas en arenas sometidas a procesos de compresión
[SPA] En esta investigación se han llevado a cabo una serie de ensayos de compresión, en condiciones edométricas, de muestras de arena preparadas con diferentes densidades y contenidos de humedad. En estos ensayos, además de las mediciones habituales de la tensión efectiva vertical y la deformación …