Results 221 to 230 of about 315,437 (261)

Sparse Modal Additive Model

IEEE Transactions on Neural Networks and Learning Systems, 2021
Sparse additive models have been successfully applied to high-dimensional data analysis due to the flexibility and interpretability of their representation. However, the existing methods are often formulated using the least-squares loss with learning the conditional mean, which is sensitive to data with the non-Gaussian noises, e.g., skewed noise ...
Hong Chen 0004, Yingjie Wang 0007, Feng Zheng 0001, Cheng Deng 0002, Heng Huang 0001   +4 more
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Additive models: Extensions and related models [PDF]

, 2012
We give an overview over smooth back tting type estimators in additive models. Moreover we illustrate their wide applicability in models closely related to additive models such as nonparametric regression with dependent error variables where the errors can be transformed to white noise by a linear transformation, nonparametric regression with ...
Mammen, Enno, Park, Byeong U., Schienle, Melanie   +2 more
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Fitting jump additive models

Computational Statistics & Data Analysis, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yicheng Kang, Yueyong Shi, Yuling Jiao, Wendong Li, Dongdong Xiang   +4 more
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On the Complexity of Additive Clustering Models

Journal of Mathematical Psychology, 2001
Additive clustering provides a conceptually simple and potentially powerful approach to modeling the similarity relationships between stimuli. The ability of additive clustering models to accommodate similarity data, however, typically arises through the incorporation of large numbers of parameterized clusters.
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Modeling the additivity of nonsimultaneous masking

Hearing Research, 1994
Thresholds were measured for detecting a brief 6-kHz sinusoidal signal preceded by a broadband noise masker (forward masking), followed by the masker (backward masking), or both preceded by and followed by the masker (combined masking). The masker-signal interval was systematically varied.
A J, Oxenham, B C, Moore
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SpAM: Sparse Additive Models

2018
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive a method for fitting the models that is effective even when the number of covariates is larger than the sample size.
Pradeep Ravikumar, Han Liu 0001, John D. Lafferty, Larry A. Wasserman   +3 more
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Additive Hazard Regression Models

1997
In the last two chapters, we examined regression models for survival data based on a proportional hazards model. In this model, the effect of the covariates was to act multiplicatively on some unknown baseline hazard rate. Covariates which do not act on the baseline hazard rate in this fashion were modeled either by the inclusion of a time-dependent ...
John P. Klein, Melvin L. Moeschberger
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Additive and generalized additive models: A survey [PDF]

, 1998
This paper is the attempt to summarize the state of art in additive and generalized additive models (GAM). The emphasis is on approaches and numerical procedures which have emerged since the monograph of Hastie and Tibshirani (1990) although reconsidering certain aspects of their work.
Schimek, Michael G., Turlach, Berwin A.
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Mechanistic models for additive manufacturing of metallic components

Progress in Materials Science, 2021
Tuhin Mukherjee, Gerald L Knapp, Amitava De   +2 more
exaly  

How Additive Manufacturing Technology Changes Business Models? – Review of Literature

Additive Manufacturing, 2020
Jyrki Savolainen, Mikael Collan
exaly