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Relevant bibliographies by topics / Distributed Lag Non-Linear Model

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Academic literature on the topic 'Distributed Lag Non-Linear Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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Journal articles on the topic "Distributed Lag Non-Linear Model"

1

Gasparrini, A., B. Armstrong, and M. G. Kenward. "Distributed lag non-linear models." Statistics in Medicine 29, no. 21 (2010): 2224–34. http://dx.doi.org/10.1002/sim.3940.

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2

Gasparrini, Antonio. "Distributed Lag Linear And Non-Linear Models With Penalized Splines." ISEE Conference Abstracts 2015, no. 1 (2015): 3069. http://dx.doi.org/10.1289/isee.2015.2015-3069.

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3

Gasparrini, Antonio. "Modeling exposure–lag–response associations with distributed lag non‐linear models." Statistics in Medicine 33, no. 5 (2013): 881–99. http://dx.doi.org/10.1002/sim.5963.

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4

Guo, Chao-Yu, Xing-Yi Huang, Pei-Cheng Kuo, and Yi-Hau Chen. "Extensions of the distributed lag non-linear model (DLNM) to account for cumulative mortality." Environmental Science and Pollution Research 28, no. 29 (2021): 38679–88. http://dx.doi.org/10.1007/s11356-021-13124-0.

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AbstractThe effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.

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5

Gasparrini, Antonio, Fabian Scheipl, Ben Armstrong, and Michael G. Kenward. "A penalized framework for distributed lag non-linear models." Biometrics 73, no. 3 (2017): 938–48. http://dx.doi.org/10.1111/biom.12645.

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6

Yan, Dawen, Guotai Chi, and Kin Keung Lai. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models." Mathematics 8, no. 8 (2020): 1275. http://dx.doi.org/10.3390/math8081275.

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In this paper, we propose a new framework of a financial early warning system through combining the unconstrained distributed lag model (DLM) and widely used financial distress prediction models such as the logistic model and the support vector machine (SVM) for the purpose of improving the performance of an early warning system for listed companies in China. We introduce simultaneously the 3~5-period-lagged financial ratios and macroeconomic factors in the consecutive time windows t − 3, t − 4 and t − 5 to the prediction models; thus, the influence of the early continued changes within and outside the company on its financial condition is detected. Further, by introducing lasso penalty into the logistic-distributed lag and SVM-distributed lag frameworks, we implement feature selection and exclude the potentially redundant factors, considering that an original long list of accounting ratios is used in the financial distress prediction context. We conduct a series of comparison analyses to test the predicting performance of the models proposed by this study. The results show that our models outperform logistic, SVM, decision tree and neural network (NN) models in a single time window, which implies that the models incorporating indicator data in multiple time windows convey more information in terms of financial distress prediction when compared with the existing singe time window models.

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7

Chien, Lung-Chang, Yuming Guo, Xiao Li, and Hwa-Lung Yu. "Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data." Journal of Exposure Science & Environmental Epidemiology 28, no. 1 (2016): 13–20. http://dx.doi.org/10.1038/jes.2016.62.

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8

Allen, Michael J., and Scott C. Sheridan. "Mortality risks during extreme temperature events (ETEs) using a distributed lag non-linear model." International Journal of Biometeorology 62, no. 1 (2015): 57–67. http://dx.doi.org/10.1007/s00484-015-1117-4.

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9

Aghababaeian, Hamidreza, Abbas Ostadtaghizadeh, Ali Ardalan, et al. "Effect of Dust Storms on Non-Accidental, Cardiovascular, and Respiratory Mortality: A Case of Dezful City in Iran." Environmental Health Insights 15 (January 2021): 117863022110601. http://dx.doi.org/10.1177/11786302211060152.

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Background: Despite the fact that Iran has been exposed to severe dust storms during the past 2 decades, few studies have investigated the health effects of these events in Iran. This study was conducted to assess the association between dust storms and daily non-accidental, cardiovascular, and respiratory mortality in Dezful City (Khuzestan Province, Iran) during 2014 to 2019. Methods: In this study, mortality, meteorological, and climatological data were obtained from the Dezful University of Medical Sciences, Iranian Meteorological Organization, and Department of Environment in Khuzestan Province, respectively. Days of dust storm were identified based on the daily concentration threshold of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) according to Hoffmanns҆ definition, and then an ecological time-series was used to estimate the short-term effects of dust storms on daily mortality. Statistical analysis was performed using a distributed lag linear model (DLM) and a distributed lag non-linear model (DLNM) packages by R software and the study results were reported as excess mortality. Results: During the study period, 15 223 deaths were recorded, and 139 dust storms occurred in Dezful city. In addition, there was statistically significant excess risk of mortality due to dust storms in Dezful City (mortality in the group under 15 years of age, lag4: 34.17% and 15-64 years of age groups, lag5: 32.19%, lag6: 3.28%), also dust storms had statistically significant effects on respiratory mortality (lag6: 5.49%). Conclusion: The findings of the current study indicate that dust storms increase the risk of mortality with some lags. An evidence-based early warning system may be able to aware the people of the health effects of dust storms.

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10

Chikri, Hassan, Adil Moghar, Manar Kassou, and Faris Hamza. "New evidence from NARDL model on CO2 emissions: Case of Morocco." E3S Web of Conferences 234 (2021): 00026. http://dx.doi.org/10.1051/e3sconf/202123400026.

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The main objective of this study is to examine the effect of sickle energy consumption, renewable energy, and forest area on the emission of carbon dioxide (CO2) in Morocco. Many studies have abord this subject using a different approachs, most of which have used econometric models such as Vector Autoregressive (VAR) Error Correction Model (ECM) and Autoregressive Distributed Lag (ARDL). In this study, we opted for the Non-linear Autoregressive Distributed Lag (NARDL) model. The data used covers the period from 1990 to 2018 (annual data). The results of our model are significant and prove the asymmetric effects of the explanatory variables on CO2 emissions.

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