Results 31 to 40 of about 777 (154)
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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A semiparametric cluster detection method — a comprehensive power comparison with Kulldorff's method
International Journal of Health Geographics, 2009 Background A semiparametric density ratio method which borrows strength from two or more samples can be applied to moving window of variable size in cluster detection. The method requires neither the prior knowledge of the underlying distribution nor the
Kedem Benjamin, Wen Shihua
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JTAM (Jurnal Teori dan Aplikasi Matematika)Semiparametric path analysis is a combination of parametric and nonparametric path analysis. Semiparametric path analysis is used when there are partially nonlinear and unknown patterns of relationships.
Dea Saraswati Pramaningrum +3 more
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SEMIPARAMETRIC IDENTIFICATION AND FISHER INFORMATION [PDF]
Econometric Theory, 2016 This paper provides a systematic approach to semiparametric identification that is based on statistical information as a measure of its “quality.” Identification can be regular or irregular, depending on whether the Fisher information for the parameter is positive or zero, respectively.
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Business Strategy and the Environment, EarlyView.ABSTRACT Whether corporate carbon management can enhance productive efficiency is central to firms' long‐term competitiveness and determines whether carbon reduction efforts can be sustained beyond regulatory compliance. This study examines how corporate carbon risk and opportunity management affects firm productivity (measured by total factor ...
Nan Huang, Hanlu Fan, Ruoxin Zhu
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Proceedings of the International Conference on Applied Innovations in ITThis paper investigates and compares the performance of two estimation approaches - Long Short-Term Memory (LSTM) networks and the semi-parametric Minimum Average Variance Estimation (SMAVE) method - for the Partial Linear Single Index Model (PLSIM) in ...
Hussein Jabbar Bayyoodh +1 more
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Semiparametric Regression Analysis via Infer.NET
Journal of Statistical Software, 2018 We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models.
Jan Luts +3 more
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Semiparametric Additive Regression
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1992 SUMMARY A simple estimator for β is proposed for the model y = x'β + g(t)+ error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a ***semiparametric likelihood and in the simplest case reduces to minimizing the distance between the 'pseudoresiduals' y - x'β and a local linear cross-validated ...
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Business Strategy and the Environment, EarlyView.ABSTRACT Small enterprises (SEs) constitute a major component of economic systems, and their socio‐environmental commitment is critical for promoting societal well‐being. This paper examines the direct effect of sound financial practices on socio‐environmental commitment and evaluates the mediating role of financial constraints—specifically debt and ...
Marcos Álvarez‐Espiño +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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