Results 1 to 10 of about 19,219 (292)
Adaptive Semiparametric Language Models [PDF]
Transactions of the Association for Computational Linguistics, 2021 We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture.
Dani Yogatama +2 more
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Determining factors associated with the number of oocytes with appropriate morphology in infertile women: A cross-sectional study [PDF]
International Journal of Reproductive BioMedicineBackground: Infertility affects millions of couples worldwide. The number of morphologically normal oocytes (MNO) is a key determinant of assisted reproductive technology success. Accurate identification of influencing factors is limited by excess zeros
Zahra Asadollahi +6 more
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Combining semiparametric and machine learning approaches for short-term prediction of satellite clock bias [PDF]
Scientific ReportsAccurate modeling of satellite clock bias (SCB) is critical for enhancing high-precision positioning capabilities. Existing approaches, such as semiparametric adjustment models and neural networks, address the nonlinearity and non-stationarity of SCB ...
Lihong Jin +5 more
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Remote Sensing, 2021 Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas.
Francisco Mauro +7 more
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Semiparametric Additive Beta Regression Models
Revstat Statistical Journal, 2021 In this paper, we study a semiparametric additive beta regression model using a parameterization based on the mean and a dispersion parameter. This model is useful for situations where the response variable is continuous and restricted to the unit ...
Germán Ibacache-Pulgar +2 more
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REGION, 2022 This article introduces a new R package (pspatreg) for the estimation of semiparametric spatial autoregressive models. pspatreg fits penalized spline semiparametric spatial autoregressive models via Restricted Maximum Likelihood or Maximum Likelihood ...
Román Mínguez +2 more
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Energies, 2020 We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three ...
Lu Yang, Shigeyuki Hamori
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Generalizing sample tree information with semiparametric and parametric models.
Silva Fennica, 1995 Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model.
Kangas, Annika, Korhonen, Kari
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bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors
Journal of Statistical Software, 2019 The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set
Seongil Jo +3 more
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Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Mathematics, 2022 As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health.
Yunquan Song, Yaqi Liu, Hang Su
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