Results 31 to 40 of about 62,879 (156)
Semina: Ciências Exatas e Tecnológicas, 2021 Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models.
Fernanda V. Roquim +5 more
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SPATIAL REGRESSION APPROACH TO MODELLING POVERTY IN JAVA ISLAND 2022
BarekengGeographically Weighted Regression (GWR) model is a powerful tool for analyzing spatial patterns in data. However, the standard form of a spatial model that uses a single bandwidth calibration may be unrealistic because the response-predictor ...
Maria A. Hasiholan Siallagan +1 more
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds [PDF]
Hydrology and Earth System Sciences, 2016 In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based ...
J. E. Shortridge +2 more
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GMM Estimation of a Partially Linear Additive Spatial Error Model
Mathematics, 2021 This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM).
Jianbao Chen, Suli Cheng
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Quantile Generalized Additive Model a Robust Alternative to Generalized Additive Model
International Journal of Mathematical Research, 2021 Nonparametric regression is an approach used when the structure of the relationship between the response and the predictor variable is unknown. It tries to estimate the structure of this relationship since there is no predetermined form. The generalized additive model (GAM) and quantile generalized additive (QGAM) model provides an attractive framework
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Online Generalized Additive Model
, 2021 Additive models and generalized additive models are effective semiparametric tools for multidimensional data. In this article we propose an online smoothing backfitting method for generalized additive models with local polynomial smoothers. The main idea is to use a second order expansion to approximate the nonlinear integral equations to maximize the ...
Yang, Ying, Yao, Fang
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The Journal of Engineering, 2021 Enhancing the grid resilience against hurricane events proactively requires a pre‐disaster system optimization built on accurate system network line outage prediction. In the past, the statistical system level failure predictive model such as generalized
Okeolu Samuel Omogoye +2 more
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Generalized functional additive mixed models
Electronic Journal of Statistics, 2016 We propose a comprehensive framework for additive regression models for non-Gaussian functional responses, allowing for multiple (partially) nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data as well as linear and nonlinear effects of functional and scalar ...
Scheipl, Fabian +2 more
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Generalized AMMI Models for Assessing The Endurance of Soybean to Leaf Pest
Jurnal Ilmu Dasar, 2010 AMMI(Additive Main Effect Multiplicative Interaction)model for interactions in two-way table provide the major mean for studying stability and adaptability through genotype × environment interaction (GEI), which modeled by full interaction model ...
Alfian Futuhul Hadi +2 more
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Geoacoustic inversion with generalized additive models [PDF]
The Journal of the Acoustical Society of America, 2019 Geoacoustic parameter estimation is presented as a non-linear regression problem where prediction is performed using generalized additive models applied to features extracted from broadband acoustic time-series in a machine learning framework. Qualitatively, it can be seen that signals that have propagated in different environments have distinct ...
Jacob, Piccolo +2 more
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