期刊文献+
共找到7篇文章
< 1 >
每页显示 20 50 100
Peer Pressure and Harmful Use of Alcohol in Thailand: A Spatial Autoregressive Model Application
1
作者 Ravikan Nonkhuntod Suchuan Yu 《International Journal of Mental Health Promotion》 2023年第5期613-626,共14页
Due to peer pressure playing a crucial role in the decision to drink,people who have a more fragile temperament might be expected to be at higher risk.Moreover,many studies have investigated the influence of peer press... Due to peer pressure playing a crucial role in the decision to drink,people who have a more fragile temperament might be expected to be at higher risk.Moreover,many studies have investigated the influence of peer pressure on alcohol consumption,but few have examined the relationship between heavy drinking and peer pressure via a spatial autoregressive model(SAR)in low/middle-income countries,such as Thailand.This paper investigated the connection between heavy drinkers over the age of 15 years who drink more than or equal to 60 grams of unmixed alcohol at least once per month based on the Thai Survey of Cigarette Smoking and Alcoholic Drinking Behavior,2014.Further,the drinkers were assumed to socialize with two peer groups:immediate family and close friends.Our paper considered a SAR model because SAR can overcome the reflection problem encountered using a linear-in-means model and the correlated effect problem found with hierarchical models.The mainfinding was the discovery of a significant and positive peer effect on alcohol consumption among heavy drinkers.In addition,there was evidence of education having an effect,but no evidence of income affecting on alcohol consumption.Specifically,a higher level of education was linked with lower levels of alcohol consumption.The results not only help us to understand the peer effect and alcohol consumption behavior,but policymakers can also apply peer effect-based strategies to formulate effective policies to decrease the alcohol consumption rate in Thailand. 展开更多
关键词 Thailand alcohol consumption heavy drinking spatial autoregressive
下载PDF
Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment
2
作者 Azadeh Atabati Hamed Adab +1 位作者 Ghasem Zolfaghari Mahdi Nasrabadi 《Water Science and Engineering》 EI CAS CSCD 2022年第3期218-227,共10页
Nitrate nitrogen(NO_(3)^(-)N)from agricultural activities and in industrial wastewater has become the main source of groundwater pollution,which has raised widespread concerns,particularly in arid and semi-arid river ... Nitrate nitrogen(NO_(3)^(-)N)from agricultural activities and in industrial wastewater has become the main source of groundwater pollution,which has raised widespread concerns,particularly in arid and semi-arid river basins with little water that meets relevant standards.This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran.To perform the modeling of the groundwater's NO_(3)^(-)N concentration,both natural and anthropogenic factors affecting groundwater NO_(3)^(-)N were selected.The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells,distance from streams,total annual precipitation,and distance from roads in the study area.This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available.Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control. 展开更多
关键词 GROUNDWATER NITRATE Natural and anthropogenic factors spatial autoregression models spatial autocorrelation
下载PDF
Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model 被引量:2
3
作者 FENG Yongjiu CHEN Xinjun LIU Yang 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2018年第3期942-955,共14页
The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. ... The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. Three typical oceanographic factors aff ecting the squid habitat were investigated in this research, including sea surface temperature(SST), sea surface salinity(SSS) and sea surface height(SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive(SAR) model and a generalized additive model(GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas off shore Peru, and off er a new SAR modeling method for advancing fishery science. 展开更多
关键词 Dosidicus gigas spatiotemporal distribution generalized additive model (GAM) spatial autoregressive(SAR) model offshore Peru
下载PDF
Using Spatial Data Mining to Predict the Solvability Space of Preconditioned Sparse Linear Systems
4
作者 Shuting Xu SangBaeKim Jun Zhang 《Computer Technology and Application》 2016年第3期139-148,共10页
The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods [1] are considered th... The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods [1] are considered the preferred methods. Selecting an effective preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The purpose of this paper is to predict the parameter solvability space of the preconditioners with two or more parameters. The parameter solvability space is usually irregular, however, in many situations it shows spatial locality, i.e. the parameter locations that are closer in parameter space are more likely to have similar solvability. We propose three spatial data mining methods to predict the solvability of ILUT which make usage of spatial locality in different ways. The three methods are MSC (multi-points SVM classifier), OSC (overall SVM classifier), and OSAC (overall spatial autoregressive classifier). The experimental results show that both MSC and OSAC can obtain 90% accuracy in prediction, but OSAC is much simpler to implement. We focus our work on ILUT preconditioner [2], but the proposed strategies should be applicable to other preconditioners with two or more parameters. 展开更多
关键词 PRECONDITIONER PREDICTION SOLVABILITY SVM spatial autoregressive model.
下载PDF
Statistical Inference of Partially Linear Spatial Autoregressive Model Under Constraint Conditions
5
作者 LI Tizheng CHENG Yaoyao 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2023年第6期2624-2660,共37页
In many application fields of regression analysis,prior information about how explanatory variables affect response variable of interest is often available and can be formulated as constraints on regression coefficien... In many application fields of regression analysis,prior information about how explanatory variables affect response variable of interest is often available and can be formulated as constraints on regression coefficients.In this paper,the authors consider statistical inference of partially linear spatial autoregressive model under constraint conditions.By combining series approximation method,twostage least squares method and Lagrange multiplier method,the authors obtain constrained estimators of the parameters and function in the partially linear spatial autoregressive model and investigate their asymptotic properties.Furthermore,the authors propose a testing method to check whether the parameters in the parametric component of the partially linear spatial autoregressive model satisfy linear constraint conditions,and derive asymptotic distributions of the resulting test statistic under both null and alternative hypotheses.Simulation results show that the proposed constrained estimators have better finite sample performance than the unconstrained estimators and the proposed testing method performs well in finite samples.Furthermore,a real example is provided to illustrate the application of the proposed estimation and testing methods. 展开更多
关键词 Constraint conditions partially linear spatial autoregressive model series estimation spatial correlation two-stage least squares
原文传递
Inference for Spatial Autoregressive Models with Infinite Variance Noises
6
作者 Gui Li LIAO Qi Meng LIU Rong Mao ZHANG 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2020年第12期1395-1416,共22页
A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a s... A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a stable law with index of stabilityα,α∈(0,2).It is shown that when the model is stationary,the self-weighted quantile estimate of the parameter has a closed form and converges to a normal limiting distribution,which avoids the difficulty of Roknossadati and Zarepour(2010)in deriving their limiting distribution for an M-estimate.On the contrary,we show that when the model is not stationary,the proposed estimates have the same limiting distributions as those of Roknossadati and Zarepour.Furthermore,a Wald test statistic is proposed to consider the test for a linear restriction on the parameter,and it is shown that under a local alternative,the Wald statistic has a non-central chisquared distribution.Simulations and a real data example are also reported to assess the performance of the proposed method. 展开更多
关键词 spatial autoregressive model heavy-tailed noise self-weighted quantile inference Wald statistic
原文传递
A case study on the shareholder network effect of stock market data:An SARMA approach
7
作者 Rong Zhang Jing Zhou +1 位作者 Wei Lan Hansheng Wang 《Science China Mathematics》 SCIE CSCD 2022年第11期2219-2242,共24页
One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models hav... One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data. 展开更多
关键词 spatial autoregressive moving average model shareholder network effect quasi-maximum likelihood estimator stock market data
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部