Poisson Estimator Research Articles

Nonparametric estimation of the gravity model of migration: application of partitioning Poisson estimates to Japanese inter-municipal migration by age–sex group

Nonparametric estimation of the gravity model of migration: application of partitioning Poisson estimates to Japanese inter-municipal migration by age–sex group

Human development and disaster mortality: evidence from India

Natural disasters present substantial dangers to both human life and physical infrastructure. Although the human development index (HDI) is recognized for its pivotal role in mitigating mortality resulting from natural disasters, the exact extent of its impact on fatalities remains unclear. In this study, we investigate the relationship between HDI and fatalities resulting from floods and cyclones using panel data for 19 states of India spanning from 1983 to 2011. Employing Fixed Effects Poisson and Negative Binomial estimates, we establish a causal-effect relationship between HDI and disaster-related fatalities. Additionally, we utilize the Instrumental Variable Poisson (IV) model to address the endogeneity between HDI and fatalities. Our empirical findings indicate that states with higher HDI levels experience lower fatalities due to natural disasters. Furthermore, our results underscore the importance of critical policy discussions regarding the role of inequality-adjusted HDI, government responsiveness, and human capital development in disaster risk reduction strategies.

ParametersEstimation of a Proposed Non-Homogeneous Poisson Process and Estimation of the Reliability Function Using the Gompertz Process: A Comparative Analysis of Artificially Intelligent and Traditional Methods

This work presents a novel approach to enhancing the rate of occurrence of non-homogeneousPoisson processes (NHPP) by utilizing the Gompertz distribution as the rate of occurrence. The primary objectiveof this study is to determine the parameters of the new process using both traditional methods and intelligenttechnology, specifically particle swarm optimization (PSO). Additionally, the study aims to estimate the reliabilityfunction of the process. The suggested model is simulated to achieve these goals, and the results are comparedamong various estimation techniques to identify the most accurate estimator. The study demonstrates that whenpredicting the time rate of occurrence of the proposed Gompertz process and its reliability function, the PSOalgorithm outperforms other approaches. Furthermore, this research showcases a practical application utilizing realdata from the Mosul power facility. Specifically, the data pertains to the stoppage times of two consecutive units ofthe Mosul Dam power stations from January 1st, 2021 to January 1st, 2022. Overall, this study introduces a novelprocess based on the Gompertz distribution to improve the rate of occurrence of NHPP. It employs particle swarmoptimization to calculate the process parameters and estimate the reliability function. The superiority of the PSOalgorithm is demonstrated through comprehensive comparisons. The practical application using data from theMosul power facility further validates the effectiveness of the proposed approach.

Consistency of the fixed effects Poisson estimator with multiplicative measurement error and unbalanced panels

We study the asymptotic properties of the popular fixed effects Poisson (FEP) estimator in two scenarios important for empirical research. First, we allow for multiplicative measurement error and characterize when the FEP estimator consistently estimates the parameters. Second, we allow for unbalanced panels and characterize when the estimator is consistent under the complete cases. We propose simple tests to determine whether selection is systematically related to unobserved time-varying shocks, which can cause the FEP estimator to be inconsistent. A simulation shows that the predictions of the theoretical findings hold in reasonable sample sizes.

Evaluating the impacts of imported cultural goods on carbon emission: Cross-country evidence

Low attention has been paid to the environmental effects of imported cultural commodities, which serve as a medium for cultural engagement and information sharing around the globe. Using the generalized method of moment (GMM) approach, this article empirically assesses the effects of imported cultural products on carbon emissions using cross-country panel data from 2004 to 2017 for 121 countries. The empirical findings reveal that the environmental consequences of imported cultural commodities vary among income level country groups. Overall, a 1% increase in the imports of total cultural goods imports would decrease carbon emissions significantly by 0.002%. After dissecting the sample into two groups, we find that a 1% increase in total cultural goods imports would lead to a significant decrease in carbon emissions of higher-income countries by 0.008%, but a significant increase in the emissions of carbon dioxide of lower-income countries by 0.002%, thus showing the importance of worldwide cultural interaction to carbon emissions. In addition, we find that performance & celebration goods and audiovisual & interactive media goods are primarily responsible for the inhibiting effects of imported cultural goods on the carbon emissions of higher-income countries. Imports of the majority of cultural products (with the exception of heritage items) would result in a significant rise in carbon emissions for low-income nations, a conclusion that is consistent with that of overall cultural goods imports. Gross domestic product per capita and economic complexity index have consistently negative effects on carbon emissions across several models for both nation groups, although energy consumption is the most significant contributor to carbon emissions. In addition, using Lasso Poisson regressions, we undertake robustness tests to assess the dependability of our empirical results based on the GMM methodology. The Lasso Poisson estimation results are consistent with our earlier findings.

Collisions and cannabis: Measuring the effect of recreational marijuana legalization on traffic crashes in Washington State

Objective Washington State was among the first states in the US to legalize recreational consumption and retail sales of marijuana. Recreational use of cannabis was legalized December 6, 2012, following the passage of Initiative 502 30 days prior. Roughly 19 months later the first retail cannabis stores opened their doors for public sales (“commercialization”). I measure the impact of cannabis legalization and commercialization on traffic collisions in Washington State. Methods With county-level vehicle crash data from the Washington State Department of Transportation collected monthly, I utilize an interrupted time-series framework with Poisson estimation to compare traffic collisions with recreational retail cannabis sales revenue from 2011 (three years pre-commercialization) through 2017 (three years post-commercialization). First, I measure the shift in collisions brought about by Washington’s 2012 cannabis legalization. Then, I compare retail cannabis sales—a measure of commercialization—to traffic collisions based on severity of injury (fatal, severe injury, minor injury, non-injury, and all). Results After controlling for confounding factors, evidence suggests that recreational cannabis legalization led to fewer fatal and serious injury collisions. Retail cannabis sales generally correlate with more traffic collisions, particularly for less severe (minor injury) crashes. These findings are robust to the inclusion of additional control variables pertaining to county-level cannabis usage and driving behavior while intoxicated. Conclusions Cannabis legalization led to fewer fatal, serious, and minor injury collisions. Commercialization (cannabis sales) correlated with an increase in less severe crashes. Although cannabis use generally increased in Washington State following legalization/commercialization, survey data suggest that driving behavior while under the influence of cannabis did not change significantly over the post-commercialization period. Future research should focus on measuring the dose-dependent impact of cannabis consumption on traffic collisions. This should include recognition of the importance of cannabis dosing, timing, and route of consumption. Lastly, the dangers of poly-drug driving—particularly cannabis and alcohol—are well established and should be high priority for further research.

Convergence analysis of pressure reconstruction methods from discrete velocities

Magnetic resonance imaging allows the measurement of the three-dimensional velocity field in blood flows. Therefore, several methods have been proposed to reconstruct the pressure field from such measurements using the incompressible Navier–Stokes equations, thereby avoiding the use of invasive technologies. However, those measurements are obtained at limited spatial resolution given by the voxel sizes in the image. In this paper, we propose a strategy for the convergence analysis of state-of-the-art pressure reconstruction methods. The methods analyzed are the so-called Pressure Poisson Estimator (PPE) and Stokes Estimator (STE). In both methods, the right-hand side corresponds to the terms that involving the field velocity in the Navier–Stokes equations, with a piecewise linear interpolation of the exact velocity. In the theoretical error analysis, we show that many terms of different order of convergence appear. These are certainly dominated by the lowest-order term, which in most cases stems from the interpolation of the velocity field. However, the numerical results in academic examples indicate that only the PPE may profit of increasing the polynomial order, and that the STE presents a higher accuracy than the PPE, but the interpolation order of the velocity field always prevails. Furthermore, we compare the pressure estimation methods on real MRI data, assessing the impact of different spatial resolutions and polynomial degrees on each method. Here, the results are consistent with the academic test cases in terms of sensitivity to polynomial order as well as the STE showing to be potentially more accurate when compared to reference pressure measurements.

Non-homogeneous Poisson process intensity modeling and estimation using measure transport

Non-homogeneous Poisson processes are used in a wide range of scientific disciplines, ranging from the environmental sciences to the health sciences. Often, the central object of interest in a point process is the underlying intensity function. Here, we present a general model for the intensity function of a non-homogeneous Poisson process using measure transport. The model is built from a flexible bijective mapping that maps from the underlying intensity function of interest to a simpler reference intensity function. We enforce bijectivity by modeling the map as a composition of multiple bijective maps that have increasing triangular structure, and show that the model exhibits an important approximation property. Estimation of the flexible mapping is accomplished within an optimization framework, wherein computations are efficiently done using tools originally designed to facilitate deep learning, and a graphics processing unit. Point process simulation and uncertainty quantification are straightforward to do with the proposed model. We demonstrate the potential benefits of our proposed method over conventional approaches to intensity modeling through various simulation studies. We also illustrate the use of our model on a real data set containing the locations of seismic events near Fiji since 1964.

Importance of temporary and permanent snow for new second homes

This study investigates empirically how natural snow depth and permanent snow affect the number of new second homes in Norway. One out of four Norwegian municipalities is partly covered by glaciers and permanent snow. In the winter seasons of 1983–2020, there is a decline in snow depth from 50 to 35 cm on average (based on 41 popular second-home areas in the mountains). Results of the fixed effects Poisson estimator with spatial elements show that there is a significant and positive relationship between natural snow depth in the municipality and the number of second homes started. There is also a significant and negative relationship between the number of new second homes in the municipality and a scarcity of snow in the surrounding municipalities. However, the magnitude of both effects is small. Estimates also show a strong positive relationship between the proportion of surface covered by permanent snow or glaciers in the municipality and new second homes. This implies that a decline in permanent snow and glaciers may make these areas less attractive for the location of second homes.

Socio-economic Determinants of Smoking Prevalence and Intensity in Nepal

This study examines the socioeconomic determinants of smoking and smoking intensity in Nepal. Leveraging data from the nationally representative Nepal Multiple Indicator Cluster Survey 2019, a logistic regression is utilized to identify predictors of individual smoking while the study relies on Poisson estimation technique to examine the determinants of smoking quantity. Our results indicate that individual characteristics—particularly age, level of education, history of alcohol use, and consumption of smokeless tobacco—play a significant role in predicting the prevalence of smoking. On the other hand, household factors like wealth scores, household size, and house-specific attributes are only weakly associated with both smoking behavior and intensity. Our results also support that similar set of socio-economic variables influence the likelihood of smoking and the quantity of cigarettes consumed. These findings emphasize the need for demand-side interventions targeted to specific socioeconomic groups, and supplementing the existing supply-side policy and regulatory measures implemented by the Government of Nepal.

Socio-economic factors influencing the adoption of low carbon technologies under rice production systems in China.

Rice (Oryza sativa L.) production, such as farmers' livelihood and the soil quality, has been identified to be strong influenced by climate change in China. However, the benefits of low carbon technologies (LCTs) are still debatable in rice production for farmers, which have been identified to tackle agricultural challenges. The choice of potential LCTs relevant to the case study is based on a literature review of previous empirical studies. Thus, the objectives of the study were to (1) investigate the public perception and preferences of LCTs in rice production of China, and (2) analyze the influences of the factors on farmer's decision in adopting LCTs in rice production. There were 555 farmer surveys from eight representative rice production counties in HP province of southern China, both the Poisson estimators and multivariate probit (MVP) approach were applied in the study. Our results show that water-saving irrigation, integrated pest management techniques and planting green manure crops in winter season were the three major LCTs adapted by farmers in rice production. The intensity and probability of LCTs adoptions were influenced by the main factors including farmers' education level, climate change awareness, machinery ownership, technical support and subsidies. There is a significant correlation among the LCTs, and the adoption of the technologies is interdependent, depicting either complementarities or substitutabilities between the practices. This study suggests that policies enhance the integration of LCTs would be central to farmers' knowledge, environmental concerns, technical service and financial support in rice production systems in China.

Características locais e a pandemia de Covid-19: uma análise voltada aos municípios do Estado brasileiro de Minas Gerais

Abstract In order to understand the reasons that led certain locations to face more/less difficulties in dealing with COVID-19, the effect of some municipal characteristics, on the main statistics related to the disease, was estimated. For this purpose, cross-section data (with cases/deaths accumulated up until April 21, 2021), on the municipalities of Minas Gerais were considered, and Ordinary Least Squares, Poisson and Negative Binomial estimators were used, in addition to the Extreme Bounds Analysis technique. Small towns, with a larger number of public health clinics (known in Brazil as “basic health units”) and more young people would have fewer cases/deaths. Urban, hot, polluted locations with higher inequality, as well as greater economic activity and movement of employees, presented the greatest problems. Incidence/mortality would increase in hot cities, with greater economic activity and a history of comorbidity. However, mortality would decrease among the youngest/most educated people. Furthermore, lethality would be lower among the younger population and in sparsely populated (up to 150,000 inhabitants) and rainless cities.

Examining the Link between Technical Efficiency, Corporate Governance and Financial Performance of Firms: Evidence from Nigeria

The purpose of this study is to examine the link between technical efficiency and both the corporate governance and financial performance of listed financial firms on the floor of the Nigerian Stock Exchange using three theoretical approaches: shareholder theory, stakeholders’ theory, and resource dependence theory. We employed a stochastic frontier analysis to examine the impact of technical efficiency on the link between corporate governance and financial performance on the one hand, and, on the other, multiple regressions comprised of OLS and Poisson estimates to analyze a data-generating set sourced from 2007 to 2020. The results of our OLS estimates suggest that a negative but significant relationship exists between the corporate governance mechanism and the financial performance of the listed firms. When we subject the analysis to the Poisson estimates, the relationship becomes positive and significant. Our results have some positive implications.

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria–Lukman estimator, and Poisson modified Kibria–Lukman estimator. Despite different Poisson biased regression estimators being proposed, there has been no analysis of the robust version of these estimators to deal with the two above-mentioned problems simultaneously, except for the robust Poisson ridge regression estimator, which we have extended by proposing three new robust Poisson one-parameter regression estimators, namely, the robust Poisson Liu (RPL), the robust Poisson Kibria–Lukman (RPKL), and the robust Poisson modified Kibria–Lukman (RPMKL). Theoretical comparisons and Monte Carlo simulations were conducted to show the proposed performance compared with the other estimators. The simulation results indicated that the proposed RPL, RPKL, and RPMKL estimators outperformed the other estimators in different scenarios, in cases where both problems existed. Finally, we analyzed two real datasets to confirm the results.

Interaction networks from discrete event data by Poisson multivariate mutual information estimation and information flow with applications from gene expression data

In this work, we introduce a new methodology for inferring the interaction structure of discrete valued time series which are Poisson distributed. While most related methods are premised on continuous state stochastic processes, in fact, discrete and counting event oriented stochastic process are natural and common, so called time-point processes. An important application that we focus on here is gene expression, where it is often assumed that the data is generated from a multivariate Poisson distribution. Nonparameteric methods such as the popular k-nearest neighbors are slow converging for discrete processes, and thus data hungry. Now, with the new multi-variate Poisson estimator developed here as the core computational engine, the causation entropy (CSE) principle, together with the associated greedy search algorithm optimal CSE (oCSE) allows us to efficiently infer the true network structure for this class of stochastic processes that were previously not practical. We illustrate the power of our method, first in benchmarking with synthetic datum, and then by inferring the genetic factors network from a breast cancer micro-ribonucleic acid sequence count data set. We show the Poisson oCSE gives the best performance among the tested methods and discovers previously known interactions on the breast cancer data set.

Role of income and government responsiveness in reducing the death toll from floods in Indian states

Floods are the most commonly occurring natural disasters in India due to India’s unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government's financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government's role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact.

The Design and Optimality of Survey Counts: A Unified Framework Via the Fisher Information Maximizer.

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for the optimum design, we propose a new two-stage algorithm, the Fisher Information Maximizer (FIM), which utilizes estimates from generalized linear models to find a global optimal grouping scheme among all possible -group schemes. After we define, decompose, and calculate different types of regressor-specific design errors, our analyses from both simulation and empirical examples suggest that: 1) the optimum design of GRC counts is able to reduce the grouping error to zero, 2) the performance of modified Poisson estimators using GRC counts can be comparable to that of Poisson regression, and 3) the optimum design is usually able to achieve the same estimation efficiency with a smaller sample size.

Racial/Ethnic disparities in drug use during the COVID 19 pandemic: Moderating effects of non-profit substance use disorder service expenditures.

The COVID-19 pandemic influenced individuals’ anxiety and depression across the United States over a short period, and some Americans relied on drugs for coping. This study examines American adults’ drug use trajectories in response to changing anxiety and depression levels during the COVID-19 pandemic and the moderating role of substance use disorder (SUD) services provided by non-profit facilities in anxiety/depression-induced drug use. Heterogeneity in such relationships is further explored based on race/ethnicity. This study used a nationally representative sample of 1,176 American adults who reported drug use between May 1, 2020, and June 30, 2021. Using individual-fixed effects Poisson estimators, the current study empirically modeled drug use changes according to changing anxiety/depression levels. Interaction terms between anxiety/depression levels and per capita spending by non-profit SUD facilities were used to explore the moderating effect of SUD service expenditures. Racial/ethnic disparities were explored in subgroup analyses on non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian samples. We found more frequent drug use in response to elevated anxiety and depression during the COVID-19 pandemic. Greater spending on SUD service by non-profit facilities at the county level was associated with reduced drug consumption associated with anxiety and depression, with greater benefits for racial/ethnic minorities. Findings provide important policy implications for distributing public funds for non-profit SUD facilities for mitigating SUD risks, especially among racial/ethnic minorities.

Finite-sample results for lasso and stepwise Neyman-orthogonal Poisson estimators

High-dimensional models that include many covariates which might potentially affect an outcome are increasingly common. This paper begins by introducing a lasso-based approach and a stepwise-based approach to valid inference for a high-dimensional model. It then discusses several essential extensions to the literature that make the estimators more usable in practice. Finally, it presents Monte Carlo evidence to help applied researchers choose which of several available estimators should be used in practice. The Monte Carlo evidence shows that our extensions to the literature perform well. It also shows that a BIC-stepwise approach performs well for a data-generating process for which the lasso-based approaches and a testing-stepwise approach fail. The Monte Carlo evidence also indicates the BIC-based lasso and plugin-based lasso can produce better inferential results than the ubiquitous CV-based lasso. Easy-to-use Stata commands are available for all the methods that we discuss.

Spherical Poisson point process intensity function modeling and estimation with measure transport

Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistics. Here, in a celebration of the ten-year anniversary of the journal Spatial Statistics, we bring together normalizing flows, commonly used for density function estimation in machine learning, and spherical point processes, a topic of particular interest to the journal’s readership, to present a new approach for modeling non-homogeneous Poisson process intensity functions on the sphere. The central idea of this framework is to build, and estimate, a flexible bijective map that transforms the underlying intensity function of interest on the sphere into a simpler, reference, intensity function, also on the sphere. Map estimation can be done efficiently using automatic differentiation and stochastic gradient descent, and uncertainty quantification can be done straightforwardly via nonparametric bootstrap. We investigate the viability of the proposed method in a simulation study, and illustrate its use in a proof-of-concept study where we model the intensity of cyclone events in the North Pacific Ocean. Our experiments reveal that normalizing flows present a flexible and straightforward way to model intensity functions on spheres, but that their potential to yield a good fit depends on the architecture of the bijective map, which can be difficult to establish in practice.