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
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
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
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
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
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
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
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China(Nos.41406146,41476129)the Natural Science Foundation of Shanghai Municipality(No.13ZR1419300)the Shanghai Universities FirstClass Disciplines Project-Fisheries(A)
文摘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.
文摘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.
基金supported by the Natural Science Foundation of Shaanxi Province under Grant No.2021JM349the Natural Science Foundation of China under Grant Nos.11972273 and 52170172。
文摘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.
基金Supported by NSFC(Grant Nos.11771390 and 11371318)Zhejiang Provincial Natural Science Foundation of China(Grant No.LR16A010001)the Fundamental Research Funds for the Central Universities。
文摘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.
基金supported by the Major Program of the National Natural Science Foundation of China (Grant No. 11731101)National Natural Science Foundation of China (Grant No. 11671349)+6 种基金supported by National Natural Science Foundation of China (Grant No. 72171226)the Beijing Municipal Social Science Foundation (Grant No. 19GLC052)the National Statistical Science Research Project (Grant No. 2020LZ38)supported by National Natural Science Foundation of China (Grant Nos. 71532001, 11931014, 12171395 and 71991472)the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economicssupported by National Natural Science Foundation of China (Grant No. 11831008)the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (Grant No. Klatasds-Moe-EcnuKlatasds2101)
文摘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.