Results 71 to 80 of about 19,219 (292)
Shape-constrained semiparametric additive stochastic volatility models
Statistical Theory and Related Fields, 2019 Nonparametric stochastic volatility models, although providing great flexibility for modelling the volatility equation, often fail to account for useful shape information.
Jiangyong Yin +3 more
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frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models
Journal of Statistical Software, 2019 When analyzing correlated time to event data, shared frailty (random effect) models are particularly attractive. However, the estimation of such models has proved challenging.
Theodor Adrian Balan, Hein Putter
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An Uncertainty Based Approach for Dealing With Selection Bias in Non‐Probability Samples
International Statistical Review, EarlyView.Summary The main issue with non‐probability samples is that the standard design‐based approach cannot be applied as the selection mechanism is unknown. In this paper, the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non‐probability sample, is discussed.
Pier Luigi Conti, Daniela Marella
wiley +1 more source
Bayesian quantile semiparametric mixed-effects double regression models
Statistical Theory and Related Fields, 2021 Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang +3 more
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A Comparative Review of Specification Tests for Diffusion Models
International Statistical Review, EarlyView.Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez +3 more
wiley +1 more source
Semiparametric prediction models for variables related with energy production
Journal of Mathematics in Industry, 2018 In this paper a review of semiparametric models developed throughout the years thanks to an extensive collaboration between the Department of Statistics and Operations Research of the University of Santiago de Compostela and a power station located in As
Wenceslao González-Manteiga +2 more
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spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Journal of Statistical Software, 2020 Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival.
Haiming Zhou +2 more
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Advances in Mathematical Physics, 2022 The nonlinear autoregressive models under normal innovations are commonly used for nonlinear time series analysis in various fields. However, using this class of models for modeling skewed data leads to unreliable results due to the disability of these ...
Leila Sakhabakhsh +3 more
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Demand Shocks, Export Diversification, and Firm Performance During the Great Trade Collapse
Journal of Economics &Management Strategy, EarlyView.ABSTRACT This paper analyzes the impact of external demand shocks from the 2008 to 2009 Great Trade Collapse on the performance of Chinese exporters and examines how export diversification mitigates these effects. Using Chinese Customs data merged with financial data from all listed Chinese firms, we construct firm‐specific demand shocks based on pre ...
Qianlin Hong +3 more
wiley +1 more source
A Semiparametric Model for Hyperspectral Anomaly Detection [PDF]
Journal of Electrical and Computer Engineering, 2012 Using hyperspectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semiparametric model under a multisample testing and minimum‐order statistic scheme. Scene anomaly detection has a wide range of use in remote sensing applications, requiring no specific material signatures ...
openaire +2 more sources