Discrete Regression Models Research Articles

Sustainable Growth in expanded BRICS: Linking Institutional performance, digital governance, and Green Finance to Environmental Impact

The main objective of the current study is to examine the impact of institutional performance, digital governance, and green finance on environmental performance within expanded BRICS nations that have undergone expansion. Key findings indicate that enhancing digital governance significantly reduces emissions; therefore, policies should be implemented to fortify digital infrastructure and promote environmentally responsible practices. The study utilized three discrete regression models to analyse the ecological impact and emissions of carbon dioxide and nitrous oxide, providing insights into the primary greenhouse gases contributing to global climate change. In light of the intricate correlation between emissions and institutional performance, it is imperative to develop nuanced policies that effectively reconcile the need to strengthen institutions with environmental concerns. Green financing is a crucial element that advocates for policies that encourage investment in environmentally friendly initiatives and technology and has a net positive impact on emissions reduction. The adoption of sustainable practices is imperative for economic expansion due to the observed correlation between heightened emissions and economic capacity. To decouple environmental degradation from economic expansion, policies should be implemented to support green technologies. In summary, these results provide evidence in favour of a comprehensive policy approach within the BRICS countries, which ought to achieve a harmonious coexistence of environmental conservation and economic advancement, to the advantage of all stakeholders. The current study is carried out to promote environmental sustainability, reduce harmful activities, strengthen institutions for collaborative pollution management, and enforce stringent environmental regulations on foreign direct investment, the expanded BRICS nations should strive to reduce detrimental activities. Placing significant emphasis on the interdependent nature of economic development and its environmental ramifications is of utmost importance.

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Abstract At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are becoming more and more prominent. This paper firstly analyzes the discrete regression model, and after comparing the logit model under random utility selection and its three derivative models, this paper selects the most widely used hybrid logit model to evaluate and analyze the online teaching platform of university public sports tennis with the MNL model of the maximum likelihood method for error analysis. Secondly, the overall architecture of the online teaching platform system is proposed, and the online teaching platform student satisfaction is studied and analyzed. Finally, based on the hybrid logit model, the AIC, BIC, and log-likelihood value indexes of the online teaching platform for college public education tennis are analyzed for the goodness of fit. The results of the study showed that students were more concerned about three aspects of the online teaching platform: operational difficulty, course comprehensiveness, and learning efficiency, which were 65%, 69%, and 74%, respectively. The AIC and BIC indicators of the hybrid logit model were 4753.656 and 4901.321, which were 192.142 and 117.848 lower than those of the conditional logit model, respectively, indicating that the model fits well and provides a better evaluation and analysis of the platform.

Diversified Integration of Civics Teaching Resources in Universities Based on Discrete Regression Model

Abstract Diversified integration, rationalized allocation and high-efficiency utilization of various practical teaching resources are the prerequisite and foundation for realizing the goal of monolithic education of Civic and Political Science teaching. This paper proposes a local two-way adaptive degree function of a complex network of Civic and Political Science teaching resources cluster and constructs a discrete regression-based resource diversification integration model based on the adaptive degree function by considering the characteristics of a complex network of Civic and Political Science teaching resources cluster with local worldliness (cluster structure), complexity, incomplete and asymmetric information obtained. The research results show that the discrete regression model Cronbach’s α coefficient value is 0.903, and the cumulative explained variance reaches 81.69%, which can effectively and highly fit the integration of diverse resources of Civics teaching. And compared with the traditional BA integration model, the discrete regression model increases the integration speed by 36.21% and the breadth by 41.39%. The discrete regression-based resource diversification integration model proposed in this paper effectively improves the level of practical teaching of Civics in colleges and universities and provides some reference and help for related researchers.

Sentiment analysis of domestic movie reviews based on a discrete regression model

Abstract In the context of the rapid development of big data information digitization, how to use big data technology to analyze the sentiment of domestic movie reviews is a hot topic of current concern. In this paper, we use two similarly themed Chinese movie reviews as the basis and use discrete regression models to study the emotional tendency of movie reviews. Firstly, the ROC curves of the regression model are plotted based on the convolutional neural network to construct the text classification task metrics and the logistic discrete regression algorithm. The prediction probability at the bottom of the curve is 56.3%, and the 95% confidence interval is [56%, 56.6%], indicating the model’s high accuracy. Then, the factors affecting effective tendency were analyzed by the logistic discrete regression model, and both user level (UL) and per capita disposable income (PCDI) had significant effects on affective tendency (ET) (both p-values were less than 0.001), and the coefficients of both influencing factors were less than 0. This indicates that user level (UL) and per capita disposable income (PCDI) have significant negative effects on affective tendency (ET) have a significant negative effect on affective tendency (ET), i.e., the higher the user level, the more likely it is to have a bad review, i.e., the higher the per capita disposable income, the more likely it is to have a bad review. This study provides a comprehensive and objective evaluation of domestic movie reviews and provides a guiding reference basis for the development direction and progress of Chinese movies.

Innovative Development Path of E-Commerce and Civil and Commercial Law in the Information Age Based on Discrete Regression Algorithm

Abstract The e-commerce industry has brought convenience, fundamentally realizing a mode of life in which purchases can be made without leaving home. Civil and commercial law is an effective guarantee to ensure that the rights and interests of buyers and sellers are not infringed so that the whole transaction process of buying and selling has clear rights and responsibilities and improves the e-commerce market. The article uses a discrete regression model to innovate the development path of e-commerce and commercial and civil law through four stages: analysis of influencing factors of e-commerce development under civil, and commercial law, hierarchical clustering analysis of e-commerce data, prediction of rights and interests of buyers and sellers in the e-commerce industry, and generation of development path integrated into civil, commercial law. An apparel department store enterprise is used as the research object to test the practical effect of the innovative development path. The adjusted goodness-of-fit coefficient of the civil and commercial law factors is 0.932, and the Pearson correlation coefficient is 0.954, which has the greatest influence on this e-commerce enterprise. The cumulative turnover in the recent year was as high as $11210142, and the realized revenue was $2045277. The turnover growth rate in the second quarter reached 251 percentage points. This is sufficient to show that the innovative research on the development path can innovate e-commerce by integrating civil and commercial law and make the e-commerce industry develop in a better direction.

Spatio-temporal evolution and location factors of port and shipping service enterprises: A case study of the Yangtze River Delta

As an important link in the global network of value chains and supply chains, the evolution of the function of modern ports depends on changes in the distribution of freight volume and shipping routes among ports, along with changes in terms of interdependence and spatial differentiation between various port and shipping service enterprises (PSSEs), such as shipping companies, multimodal transport providers, freight forwarders, and shipping financial institutions. On the basis of defining the connotation of PSSEs, this study takes the Yangtze River Delta as a research case and uses the three-axis rotation method, standard deviational ellipse, and spatial interpolation simulation to analyse the distribution pattern and evolution characteristics of PSSEs from 2002 to 2019. The discrete regression model was used to explore the main factors affecting the location choice of various PSSEs. The characteristics of the spatial and temporal evolution of PSSEs in the Yangtze River Delta are as follows: (1) The structure of the PSSEs in the Yangtze River Delta is in the process of continuous upgrading. It presents a ‘medium-high-low’ pattern in which medium-end enterprises are dominant, and high-end enterprises are increasing rapidly. (2) The spatial distribution of low- and high-end enterprises is concentrated, and medium-end enterprises are more dispersed. The distribution centre of medium- and low-end enterprises moves westward, while the distribution centre of high-end enterprises turns eastward. (3) The growth rates of medium- and high-end enterprises are much higher than those of low-end enterprises. The analysis of location factors based on the conditional logit model demonstrates that the factors affecting PSSEs are becoming increasingly complex. Besides the influence of traditional location factors such as the economy and transportation of the hinterland, they are also affected by emerging factors such as informatization and the level of financial development. Moreover, the impact of external factors, such as globalization, marketisation, and servitisation, is also rising significantly.

Modelling of Factors Influencing the Citation Counts in Statistics

Citation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to the fact that distribution of citation counts involves a great number of zeros, this study serves for an additional aim that is to model the counts with advanced discrete regression models for a more precise prediction [2]. Data collected for this study consist of the citation counts of all scientific papers produced by 261 Statisticians in between 2005-2017. Discrete models varying from Poisson to Zero-Inflated or Hurdle were constructed by possible influential factors, such as the publication age, the number of references, the journal category etc. Predictive performances of alternative discrete models were compared via AIC and Vuong test [3]. Results suggested that Zero Inflated Negative Binomial and Hurdle Negative Binomial mixture models are the best forms to predict the zero inflation of citation counts [4]. In addition, the influential factors of the final model were interpreted to make some suggestions to Statisticians to increase the citation counts of their work.

Global Oil Export Destination Prediction: A Machine Learning Approach

We use classification methods from machine learning to predict the destination of global crude oil exports by utilising micro-level crude oil shipment data that incorporates attributes related to the contract, cargo specifications, vessel specifications and macroeconomic conditions. The results show that micro-level information about the oil shipment such as quality and cargo size dominates in the destination prediction. We contribute to the academic literature by providing the first machine learning application to oil shipment data, and by providing new knowledge on the determinants of global crude oil flows. The machine-learning models used to predict the importing country can reach an accuracy of above 71% for the major oil exporting countries based on out-of-sample tests and outperform both naive models and discrete regression models.

Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students’ life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

Semiparametric multiple kernel estimators and model diagnostics for count regression functions

This study concerns semiparametric approaches to estimate discrete multivariate count regression functions. The semiparametric approaches investigated consist of combining discrete multivariate nonparametric kernel and parametric estimations such that (i) a prior knowledge of the conditional distribution of model response may be incorporated and (ii) the bias of the traditional nonparametric kernel regression estimator of Nadaraya-Watson may be reduced. We are precisely interested in combination of the two estimations approaches with some asymptotic properties of the resulting estimators. Asymptotic normality results were showed for nonparametric correction terms of parametric start function of the estimators. The performance of discrete semiparametric multivariate kernel estimators studied is illustrated using simulations and real count data. In addition, diagnostic checks are performed to test the adequacy of the parametric start model to the true discrete regression model. Finally, using discrete semiparametric multivariate kernel estimators provides a bias reduction when the parametric multivariate regression model used as start regression function belongs to a neighborhood of the true regression model.

On the estimation of normal copula discrete regression models using the continuous extension and simulated likelihood

The continuous extension of a discrete random variable is amongst the computational methods used for estimation of multivariate normal copula-based models with discrete margins. Its advantage is that the likelihood can be derived conveniently under the theory for copula models with continuous margins, but there has not been a clear analysis of the adequacy of this method. We investigate the asymptotic and small-sample efficiency of two variants of the method for estimating the multivariate normal copula with univariate binary, Poisson, and negative binomial regressions, and show that they lead to biased estimates for the latent correlations, and the univariate marginal parameters that are not regression coefficients. We implement a maximum simulated likelihood method, which is based on evaluating the multidimensional integrals of the likelihood with randomized quasi-Monte Carlo methods. Asymptotic and small-sample efficiency calculations show that our method is nearly as efficient as maximum likelihood for fully specified multivariate normal copula-based models. An illustrative example is given to show the use of our simulated likelihood method.

Market Timing For active Asset Allocation: A Discrete Regression Model Approach

A discrete regression model (DRM) approach to timing the asset class markets for stocks, bonds, and cash in active asset allocation is presented. The technique involves investing in the asset class whose return is predicted to exceed the other asset class return after observing a sequential signal of estimated probabilities. The empirical results show that the DRM approach provides enhanced portfolio performance when compared to more passive fixed-weight portfolio strategies.

Predicting Conflict in Space and Time

The prediction of conflict constitutes a challenge to social scientists. This article explores whether the incorporation of geography can help us make our forecasts of political violence more accurate. The authors describe a spatially and temporally autoregressive discrete regression model, following the framework of Geyer and Thompson. This model is applied to geo-located data on attributes and conflict events in Bosnia over the period from March 1992 to October 1995. Results show that there is a strong spatial as well as temporal dimension to the outbreak of violence in Bosnia. The authors then explore the use of this model for predicting future conflict. Using a simulation approach, the predictive accuracy of the spatial—temporal model is compared to a standard regression model that only includes time lags. The results show that even in a difficult out-of-sample prediction task, the incorporation of space improves our forecasts of future conflict.

Discrete distributions when modeling the disability severity score of motor victims

Many European countries apply score systems to evaluate the disability severity of non-fatal motor victims under the law of third-party liability. The score is a non-negative integer with an upper bound at 100 that increases with severity. It may be automatically converted into financial terms and thus also reflects the compensation cost for disability. In this paper, standard and zero-altered discrete regression models are applied to model the disability severity score of victims. An application using data from Spain is provided in which the hurdle-Negative Binomial regression was the preferred method. The effects of victims’ characteristics, type of road user and recovery duration are examined. The results suggest that the expected permanent disability severity is higher for older women with long recovery periods. The results provide traffic decision makers with a model to quantify the compensation cost savings due to disability severity reductions.

Innovation activity in the hotel industry

This paper provides evidence for different innovation activities in the hotel industry. In particular, we explore the influence of a variety of firm and market characteristics on radical and incremental innovations. We consider the learning of new attributes (radical) and the addition of characteristics to existing attributes (incremental) to represent two different paces or degrees of managing the innovation process in this specific industry. The database used in the empirical study shares the major features of new approaches about innovation in services. A questionnaire administered to a representative sample of hotel managers in the Balearic Islands provides the data for the discrete regression models used to represent the innovation in these hotels. Our main conclusion is that radical and incremental innovations appear to be interrelated. Furthermore, the main determinants of innovation are the form of hotel management, the hotel market strategy, and the size and location of the hotel.

Applications of some discrete regression models for count data

In this paper we have considered several regression models to fit the count data that encounter in the field of Biometrical, Environmental, Social Sciences and Transportation Engineering. We have fitted Poisson (PO), Negative Binomial (NB), Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) regression models to run-off-road (ROR) crash data which collected on arterial roads in south region (rural) of Florida State. To compare the performance of these models, we analyzed data with moderate to high percentage of zero counts. Because the variances were almost three times greater than the means, it appeared that both NB and ZINB models performed better than PO and ZIP models for the zero inflated and over dispersed count data.

LIKELIHOOD APPROXIMATIONS AND DISCRETE MODELS FOR TIED SURVIVAL DATA

ABSTRACT Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. On the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

Construction of J-variate distribution functions and applications to discrete decision models

The construction of J-variate distribution functions, introducing dependences among J random variables and keeping fixed the J marginal distribution functions, is important in the development of theoretical and empirical statistical analysis. Here, a method for generating such distribution functions is developed. Characteristics of the resulting distribution functions are discussed. An application to discrete regression models is presented. The latter is specialized to model choice of mode od transportation by travelers.

Small-sample confidence regions in exponential families.

This article presents an algorithm for small-sample conditional confidence regions for two or more parameters for any discrete regression model in the generalized linear interactive model family. Regions are constructed by careful inversion of conditional hypothesis tests. This method presupposes the use of approximate or exact techniques for enumerating the sample space for some components of the vector of sufficient statistics conditional on other components. Such enumeration may be performed exactly or by exact or approximate Monte Carlo, including the algorithms of Kolassa and Tanner (1994, Journal of the American Statistical Association 89, 697-702; 1999, Biometrics 55, 246-251). This method also assumes that one can compute certain conditional probabilities for a fixed value of the parameter vector. Because of a property of exponential families, one can use this set of conditional probabilities to directly compute the conditional probabilities associated with any other value of the vector of the parameters of interest. This observation dramatically reduces the computational effort required to invert the hypothesis test to obtain the confidence region. To construct a region with confidence level 1 - alpha, the algorithm begins with a grid of values for the parameters of interest. For each parameter vector on the grid (corresponding to the current null hypothesis), one transforms the initial set of conditional probabilities using exponential tilting and then calculates the p value for this current null hypothesis. The confidence region is the set of parameter values for which the p value is at least alpha.

Constructive Asymptotic Equivalence of Density Estimation and Gaussian White Noise

A recipe is provided for producing, from a sequence of procedures in the Gaussian regression model, an asymptotically equivalent sequence in the density estimation model with i. i. d. observations. The recipe is, to put it roughly, to calculate square roots of normalised frequencies over certain intervals, add a small random distortion, and pretend these to be observations from a Gaussian discrete regression model.