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Distributed Lag Linear and Non-Linear Models in R: The Package dlnm [PDF]
Journal of Statistical Software, 2011 Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data.
Antonio Gasparrini
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Distributed lag non-linear models. [PDF]
Stat Med, 2010 AbstractEnvironmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non‐linear models (DLNM), a modelling framework that can simultaneously represent non‐
Gasparrini A, Armstrong B, Kenward MG.
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Analysis of parametric biological models with non-linear dynamics [PDF]
Electronic Proceedings in Theoretical Computer Science, 2012 In this paper we present recent results on parametric analysis of biological models. The underlying method is based on the algorithms for computing trajectory sets of hybrid systems with polynomial dynamics. The method is then applied to two case studies
Thao Dang, Romain Testylier
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Falsifying cosmological models based on a non-linear electrodynamics
European Physical Journal C: Particles and Fields, 2018 Recently, the nonlinear electrodynamics (NED) has been gaining attention to generate primordial magnetic fields in the Universe and also to resolve singularity problems. Moreover, recent works have shown the crucial role of the NED on the inflation. This
Ali Övgün +3 more
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The dressing method as non linear superposition in sigma models
Journal of High Energy Physics, 2021 We apply the dressing method on the Non Linear Sigma Model (NLSM), which describes the propagation of strings on ℝ × S2, for an arbitrary seed. We obtain a formal solution of the corresponding auxiliary system, which is expressed in terms of the ...
Dimitrios Katsinis +2 more
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Probabilistic time series forecasting with deep non‐linear state space models
CAAI Transactions on Intelligence Technology, 2023 Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series observations. It is a widespread challenge in various tasks, such as risk management and decision making.
Heming Du, Shouguo Du, Wen Li
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Predicting the growth curve of body weight in madura cattle [PDF]
Kafkas Universitesi Veteriner Fakültesi Dergisi, 2021 The growth curve of livestock animals is important to evaluate the biological development managed with a farming management system. This study aimed to estimate the growth curve of body weight (BW) in Madura cattle (Bos indicus) kept at the breeding ...
Hartati HARTATI +1 more
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Genome‐based prediction of Bayesian linear and non‐linear regression models for ordinal data
The Plant Genome, 2020 Linear and non‐linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic‐enabled prediction models have been developed for predicting complex ...
Paulino Pérez‐Rodríguez +5 more
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Non-Linear Models in Economic and Social Research
Вестник Российского экономического университета имени Г. В. Плеханова, 2022 The article shows differences of presented models from models used in exact science. It analyzes the reasons why for mathematic models of economic and social systems we cannot reach the quantitative correlation of modeling results with indicators of real
L. F. Petrov
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Non Abelian T-duality in Gauged Linear Sigma Models
Journal of High Energy Physics, 2018 Abelian T-duality in Gauged Linear Sigma Models (GLSM) forms the basis of the physical understanding of Mirror Symmetry as presented by Hori and Vafa.
Nana Cabo Bizet +3 more
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