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Beta regression models are employed to model continuous response variables in the unit interval, like rates, percentages, or proportions. Their applications rise in several areas, such as medicine, environment research, finance, and natural sciences. The maximum likelihood estimation is widely used to make inferences for the parameters. Nonetheless, it is well-known that the maximum likelihood-based inference suffers from the lack of robustness in the presence of outliers. Such a case can bring severe bias and misleading conclusions. Recently, robust estimators for beta regression models were presented in the literature. However, these estimators require non-trivial restrictions in the parameter space, which limit their application. This paper develops new robust estimators that overcome this drawback. Their asymptotic and robustness properties are studied, and robust Wald-type tests are introduced. Simulation results evidence the merits of the new robust estimators. Inference and diagnostics using the new estimators are illustrated in an application to health insurance coverage data. The new R package robustbetareg is introduced. [ABSTRACT FROM AUTHOR]

Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts.We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology.Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate.We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data. [ABSTRACT FROM AUTHOR]

Abstract Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts. We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate. We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data.

The ecosystem services and immense biodiversity of Amazon rainforests are threatened by deforestation and forest degradation. A key goal of modern archaeology and paleoecology in Amazonia is to establish the extent and duration of past forest disturbance by humans. Fossil phytoliths are an established proxy to identify the duration of disturbance in lake sedimentary and soil archives. What is not known, is the spatial scale of such forest disturbances when identified by phytoliths. Here we use phytolith assemblages to detect local-scale forest openings, provide an estimate of extent, and consider long-term forest recovery. We use modern phytolith assemblages of 50 Amazonian lakes to i) assess how phytolith assemblages vary across forest cover at 5 spatial scales (100 m, 200 m, 500 m, 1 km, 2 km), ii) model which phytolith morphotypes can accurately predict forest cover at 5 spatial scales, and iii) compare phytoliths with pollen to quantify their relative ability to detect forest cover changes. DCA results show phytolith assemblages could be used to differentiate low, intermediate, and high forest cover values, but not to distinguish between biogeographical gradients across Amazonia. Beta regression models show Poaceae phytoliths can accurately predict forest cover within 200 m of Amazonian lakes. This modern calibration dataset can be used to make quantitative reconstructions of forest cover changes in Amazonia, to generate novel insights into long-term forest recovery. Combining phytoliths and pollen provides a unique opportunity to make qualitative and quantitative reconstructions of past vegetation changes, to better understand how human activities, environmental and climatic changes have shaped modern Amazonian forests.

The main objective of this paper is to introduce a new class of estimators for the ill-conditioned beta regression model. The new class, named the Liu-ridge-type (LRT), incorporates two shrinkage parameters that allow it to include the maximum-likelihood estimator (MLE), the ridge estimator (RE), and the Liu estimator (LE) as special cases. The LRT estimator has been shown to outperform the LE, the RE, and the MLE in terms of the mean squared error (MSE) matrix under certain conditions. Simulated and real applications illustrate the potential benefits of the new LRT estimator. [ABSTRACT FROM AUTHOR]

Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm accounting for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed. [ABSTRACT FROM AUTHOR]

Global Value Chain (GVC) participation has significantly influenced production patterns and specializations across various industry sectors globally. This study employs a quantitative approach using the GVC Participation Index database specific to the agricultural sector and the Global Food Security Index (GFSI) from 2012 to 2021, analyzed using Beta Regression. The results reveal that GVC participation significantly impacts food security. Simple forward and complex backward GVC participation positively influence food security, whereas forward complex participation has a negative effect. Food security varies significantly by region and income level, with Europe & Central Asia and North America, as well as high-income countries, exhibiting better food security. In contrast, South and East Asia and lower-middle-income countries show lower food security. These findings underscore the necessity for targeted policies and interventions to enhance GVC participation based on regional and income-specific conditions. [ABSTRACT FROM AUTHOR]

With the rapid growth of the gig economy in China, millions of food delivery e-bikers are making their living by rushing on the street. Speeding is one of their most common risky riding behaviours, leading to severe traffic crashes. Based on 2-month naturalistic cycling data of 46 full-time food delivery e-bikers in Changsha, their speeding behaviour is deeply studied with the individual daily speeding proportion being taken as the speeding indicator. A beta regression model is built to identify the factors significantly influencing the indicator. The estimation results reveal that female riders, middle-aged riders and riders with a bachelor’s degree are less likely to engage in speeding. The same result is indicated for those working longer or experiencing more crashes. Additionally, holidays and riding distance are found to have significantly positive influences. Finally, some countermeasures are proposed to prevent speeding among food delivery e-bikers. [ABSTRACT FROM AUTHOR]

In this paper, we study the research efficiency of the Turkish higher education sector in a two-stage DEA model with variable returns to scale. The aim of this paper is to benchmark or rank universities according to their research performance and to identify exogenous factors that may affect an institution’s efficiency score. DEA scores are a prime example of fractional data - a fact that has been disregarded by many previous DEA models which used popular Tobit regression for censored data in the second stage. Using a sample of 50 private and public universities, the first stage of our model calculates the efficiency scores and determines the efficient reference set for inefficient universities. In the second stage, we use beta regression and bootstrapped hypothesis testing to estimate the effects that external factors (age, size and ownership status) have on efficiency scores. We find that 27 universities in our sample are research efficient. Beta regression summary statistics suggest that extra-large universities tend to be less research efficient than large universities (p=0.1), while both age and ownership status of the university do not have a statistically significant impact on an institution’s efficiency score.

The nature of the variable in many regression usages is the response in terms of rate and ratio. As in economics, economists seek to understand the relationship between growth or unemployment rates and several other economic variables. Logistic and Probit models are usually used to model data with a variation range (0,1). However, Logistic and Probit models are not suitable for rate or ratio data modeling due to their concentration in a certain sub-range of their variation ranges. Regarding the high flexibility of the beta distribution for this type of data, a proper and efficient model is a regression based on beta distribution, which is called beta regression. This paper introduces the model and estimates its parameters. Then, we obtain an asymptotic distribution of estimators and evaluate the performance of the proposed model based on the MSE index using simulation. In the end, we will show the use of the model in a real example of concrete strength data. [ABSTRACT FROM AUTHOR]

Continuous responses measured on a standard unit interval (0,1)$$ \left(0,1\right) $$ are ubiquitous in many scientific disciplines. Statistical models built upon a normal error structure do not generally work because they can produce biassed estimates or result in predictions outside either bound. In real‐life applications, data are often high‐dimensional, correlated and consist of a mixture of various data types. Little literature is available to address the unique data challenge. We propose a semiparametric approach to analyse the association between a double‐bounded response and high‐dimensional correlated covariates of mixed types. The proposed method makes full use of all available data through one or several linear combinations of the covariates without losing information from the data. The only assumption we make is that the response variable follows a Beta distribution; no additional assumption is required. The resulting estimators are consistent and efficient. We illustrate the proposed method in simulation studies and demonstrate it in a real‐life data application. The semiparametric approach contributes to the sufficient dimension reduction literature for its novelty in investigating double‐bounded response which is absent in the current literature. This work also provides a new tool for data practitioners to analyse the association between a popular unit interval response and mixed types of high‐dimensional correlated covariates. [ABSTRACT FROM AUTHOR]

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended. [ABSTRACT FROM AUTHOR]

The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically relevant metadata. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself in an unsupervised fashion, usually by common topic models. A frequently used procedure to achieve this is the method of composition, a Monte Carlo sampling technique performing multiple repeated linear regressions of sampled topic proportions on metadata covariates. In this paper, we propose two modifications of this approach: First, we substantially refine the existing implementation of the method of composition from the R package stm by replacing linear regression with the more appropriate Beta regression. Second, we provide a fundamental enhancement of the entire estimation framework by substituting the current blending of frequentist and Bayesian methods with a fully Bayesian approach. This allows for a more appropriate quantification of uncertainty. We illustrate our improved methodology by investigating relationships between Twitter posts by German parliamentarians and different metadata covariates related to their electoral districts, using the structural topic model to estimate topic proportions. [ABSTRACT FROM AUTHOR]

In this paper, we study the research efficiency of the Turkish higher education sector in a two-stage DEA model with variable returns to scale. The aim of this paper is to benchmark or rank universities according to their research performance and to identify exogenous factors that may affect an institution’s efficiency score. DEA scores are a prime example of fractional data - a fact that has been disregarded by many previous DEA models which used popular Tobit regression for censored data in the second stage. Using a sample of 50 private and public universities, the first stage of our model calculates the efficiency scores and determines the efficient reference set for inefficient universities. In the second stage, we use beta regression and bootstrapped hypothesis testing to estimate the effects that external factors (age, size and ownership status) have on efficiency scores. We find that 27 universities in our sample are research efficient. Beta regression summary statistics suggest that extra-large universities tend to be less research efficient than large universities (p=0.1), while both age and ownership status of the university do not have a statistically significant impact on an institution’s efficiency score. [ABSTRACT FROM AUTHOR]

Beta distributions are commonly used to model proportion valued response variables, often encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a interpretable monotone time-varying single index transform of a linear combination of the potential covariates. We utilize the potential of single index models, which are effective dimension reduction tools and accommodate link function misspecification in generalized linear mixed models. Our Bayesian methodology incorporates the missing-at-random feature of the proportion response and utilize Hamiltonian Monte Carlo sampling to conduct inference. We explore finite-sample frequentist properties of our estimates and assess the robustness via detailed simulation studies. Finally, we illustrate our methodology via application to a motivating longitudinal dataset on obesity research recording proportion body fat. [ABSTRACT FROM AUTHOR]

This article presents techniques for dealing with a form of dependency in data arising when numerical data sum to a constant for individual cases, that is, "compositional" or "ipsative" data. Examples are percentages that sum to 100, and hours in a day that sum to 24. Ipsative scales fell out of fashion in psychology during the 1960s and 1970s due to a lack of methods for analyzing them. However, ipsative scales have merits, and compositional data commonly occur in psychological research. Moreover, as we demonstrate, sometimes converting data to a compositional form yields insights not otherwise accessible. Fortunately, there are sound methods for analyzing compositional data. We seek to enable researchers to analyze compositional data by presenting appropriate techniques and illustrating their application to real data. First, we elaborate the technical details of compositional data and discuss both established and new approaches to their analysis. We then present applications of these methods to real social science datasets (data and code using R are available in a supplementary document). We conclude with a discussion of the state of the art in compositional data analysis and remaining unsolved problems. A brief guide to available software resources is provided in the first section of the supplementary document. Psychological researchers sometimes must deal with numerical data that has a constant sum for each case in the sample. For instance, the amounts of time out of a 24-hr day that a person devotes to sleep, eating, work, recreation, and all other activities must sum to 24 hr. Likewise, the percentages of a person's income allocated to food, rent, clothing, transportation, all other expenses, and savings must sum to 100%. These are known as "compositional data" in some disciplines, and traditionally as "ipsative data" in psychology. Researchers in psychology during the past several decades have had difficulties in analyzing compositional data because of the constant-sum requirement, and as a result, tended to avoid this kind of data. Fortunately, straightforward techniques for analyzing compositional data have been developed since the 1980s and software resources are available for them. We elaborate these techniques and demonstrate their application to real data. We also discuss the state of the art in compositional data analysis, including unsolved problems and new approaches. This article has two goals: enabling researchers to analyze compositional data, and persuading them that analyzing data from a compositional standpoint can be useful. [ABSTRACT FROM AUTHOR]

In the context of agricultural development, economic growth, and food security in Africa, examining the practice of land exchange holds significant relevance. This study analyses the practice of land exchange and its effect on farmers' performance in Norther Eastern Zone of Nigeria. A multi-stage sampling procedure was employed to select a sample of 400 rice farmers engaged in irrigation farming. The selected farmers participated in structured interviews, providing the necessary data for the study. Descriptive analysis (of the mean) revealed that farmers are engaged in land exchange (16.07%) using two methods: land exchange for agricultural use (or farming purposes) and land exchange for property. Using a logistic regression model, it was found that number of plots, decrease in distance among plots, practice of mechanization, decrease in production costs, and improvement of efficiency were factors influencing farmers to exchange land. The result also suggested that farmers exhibited a high level of technical efficiency, implying that there is room for further enhancement in efficiency through the adoption of advanced technologies and the optimal utilization of existing resources. The beta regression's results indicated that land development have a negative effect on technical efficiency, while household size, rented land, and hired labor have positive effects. However, it was found that the practice of land exchange did not affect the level of technical efficiency of rice farmers in the study area, because of the observed limited land market and the high level of crop diversification. Hence, policymakers are advised to define land use rights explicitly and encourage land transactions, such as renting among farmers, selling occupancy rights, and transferring leasehold rights. These measures aim to improve land efficiency and bolster the land market.

Brand equity (BE) and customer equity (CE) are the two crucial and closely linked concepts in marketing research. This research outlines a new conceptual framework to explore the relationship between the critical elements of BE and CE. Furthermore, using marketing activities, the study quantifies the effect of these activities on CE. The value of CE is computed based on a customer lifetime value (CLV) model in which linear, logistic, and beta regression are used to predict BE, customer acquisition, and customer share of wallet, respectively. We conducted an empirical analysis through questionnaires in an elevator company. The results reveal that brand knowledge and brand differentiation positively relate to customer acquisition. Also, for both existing customers and prospects, brand differentiation plays an important role in the share of wallet. The findings also show that marketing activities have a positive and significant impact on brand knowledge and brand differentiation, and consequently, through the mediating role of BE between marketing activities and CE, on customer acquisition and share of wallet. [ABSTRACT FROM AUTHOR]

During primary succession, the abundance of different species and their associated plant traits change over time. Understanding how plant traits linked to colonising and competitive abilities change through succession is important for determining whether community assembly can be predicted. Examining this across more than one taxon group can reveal if these patterns are generalisable.Here, we investigated primary succession on bare chalk for a chronosequence spanning 33 years for two different taxa, vascular plants and bryophytes. We examined how abundance changed through succession, and how this related to species' colonising and competitive abilities, using relevant plant traits for each taxa. A zero‐inflated beta regression model was used to investigate the effects of traits on both presence/absence and abundance‐when‐present of vascular plants and bryophytes.Vascular plants with a larger specific leaf area were more likely to occur later in succession. Vascular plants, which were hemicryptophytes, wind dispersing and had a lower canopy height, were more likely to increase in abundance‐when‐present during succession.Bryophytes with a larger spore diameter were more likely to occur later in succession. Shorter bryophytes with a greater frequency of sporophyte production had a higher abundance early in succession, representing their high colonising abilities. Whereas later in succession larger bryophytes, with a mat or weft life form and low sporophyte frequency were more abundant, indicating a shift towards greater competitive abilities.Synthesis. This study has revealed different patterns for vascular plants and bryophytes regarding colonisation and changes in abundance through succession, and the associated traits linked to colonising and competitive abilities. Although some traits were found to influence abundance through succession for vascular plants, these were often contrary to the expected pattern representing the change from colonising to competitive abilities, whereas for bryophytes, there was more evidence for this shift with successional age. This suggests that general theories on succession‐linked plant traits should not be relied upon in isolation for the prediction of community assembly. Context, particularly successional age in relation to the available species pool is also key. [ABSTRACT FROM AUTHOR]

Quantifying drug potency, which requires an accurate estimation of dose–response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median–effect equation to describe the dose–response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose–response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression‐based methods have also been introduced in estimation of the median–effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose–response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification. [ABSTRACT FROM AUTHOR]

Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macroregion levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil, and Atkinson indexes) to obtain reliable microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at the area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage, and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions. [ABSTRACT FROM AUTHOR]

Brazil was one of the countries most impacted by the COVID-19 pandemic, with a cumulative total of nearly 700,000 deaths by early 2023. The country's federative units were unevenly affected by the pandemic and adopted mitigation measures of different scopes and intensity. There was intense conflict between the federal government and state governments over the relevance and extent of such measures. We build a simple regression model with good predictive power on state COVID-19 mortality rates in Brazil. Our results reveal that the federative units' urbanization rate and per capita income are important for determining their mean mortality rate and that the number of physicians per 100,000 inhabitants is important for modeling the mortality rate precision. Based on the fitted model, we obtain approximations for the levels of administrative efficiency of local governments in dealing with the pandemic.

AbstractWheat production is dominated by a subsistence smallholder production system. Additionally, more than 4.7 million smallholder farmers are engaged in wheat production. However, poverty is chronic and pervasive among smallholder farmers. Hence, targeting the efficiency of wheat production is the right strategy to improve the well-being of smallholder farmers. Therefore, we conducted this study to measure the level of wheat production efficiency and figure out the sources of wheat production inefficiency among smallholder farmers using stochastic frontier and beta regression models, respectively. Hence, 400 smallholder farmers were selected to gather firsthand information on wheat production and important variables. The stochastic frontier result shows that the number of oxen, amount of urea fertilizer, and seed had a positive and statistically significant effect on the level of wheat production, unlike wheat farm size. The mean technical efficiency result indicates that smallholder farmers operate 23% below the maximum capacity of wheat production. Additionally, smallholder farmers were producing 18.97 quintals per hectare less than the potential production capacity. Consequently, the beta regression model result shows that an increase in the dependency ratio, distance to the local wheat market, and distance to the extension office will increase the technical inefficiency of wheat production. On the contrary, educational status, farm experience, and access to wheat price information decrease the technical inefficiency of wheat production. Therefore, policymakers, stakeholders, and farmers should consider the main sources of technical inefficiency to minimize the sources of wheat production inefficiency.

In Ethiopia, agricultural commercialization is not well developed. Smallholder farmers with a subsistence farming system dominate crop production, resulting in incompetent and less commercialized produce. As a result, producing market-oriented products can increase the well-being of smallholder farmers. Accordingly, we conducted this study to analyze determinants of teff commercialization among smallholder farmers in Hulet Eju Enese Woreda, Ethiopia. The primary data were collected from 384 randomly selected smallholder farmers to measure the level of teff commercialization and analyze determinants of teff commercialization among smallholder farmers. To address the objectives of this study, an output commercialization index, and a beta regression model were used. The findings show that about 77.2% of smallholder farmers are classified as commercial, while semicommercial farmers account for 22.8% of all observations. Furthermore, the model results revealed that the number of oxen, teff land size, farming experience in teff production, market distance, and agroecology had statistically significant effects on teff commercialization. Therefore, sources of improved traction power, land productivity, market infrastructure, experience-sharing strategies, and new varieties that can adapt to varied agroecology should be given special priority to increase smallholder farmers’ commercialization.

The purpose of this study was to develop the best transfer functions for estimating the soil water retention curve (SWRC) for Iraqi soils using multiple regression methods. Soil samples were collected from 30 different sites in Iraq at two depths (0-0.3 m and 0.3-0.6 m) to create a database for the development of predictive transfer functions. The database included information on soil particle size distribution, carbonate minerals, mass density, particle density, organic matter, saturated hydraulic conductivity, capillary height, and available water limits. Explanatory variables (EV) were the measured characteristics, while response variables (RV) were the volumetric water content measured at different potentials (0, 5, 10, 33, 500, 1000, 1500 kPa). Two methods were used to develop predictive transfer functions: the logit model and beta model. Prediction accuracy was assessed using mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that the variables included in the derivation of the two models for predicting θ( Ψ) were similar, except at θ(0). The variables w1 (w1 = 2Psand° - Psilt° - Pcaly° - Pcarbonate), capillary height, available water, and porosity were found to be included in most of the logit and beta models. Additionally, there were no statistically significant differences between the MAE, RMSE, and R2 values of the two models. However, the beta model performed better in terms of MBE compared to the logit model. The models also demonstrated highly significant R2 values (0.9819-1.00) for a linear relationship between the measured and predicted water content values. [ABSTRACT FROM AUTHOR]

The Living Docks restoration program was implemented in the Indian River Lagoon (IRL), Florida, with the goal of affixing oyster restoration mats to dock pilings to promote the growth of filter feeding benthic organisms which can help improve local water quality. However, the relationship between IRL water quality parameters and the presence of filter feeders on the mats is not entirely clear. This study investigates the presence of benthic organisms on eight Living Docks which were deployed throughout the central part of the IRL. Environmental factors (e.g., water salinity, turbidity, pH, and temperature) were collected from the closest available water station to each dock. The main goal was to identify the presence and overall change in percent cover of specific benthic organism(s), those which are known filter feeders, in relationship to environmental parameters. Among functional groups which were identified, barnacles, biofilms, encrusting bryozoans (EBs), oysters, and sponges demonstrated significantly higher cover than the others. Barnacles were higher in abundance at specific dock locations and an increased water pH (up to 8.1), turbidity, and temperature. EB presence was positively impacted by salinity but did not respond to changes in turbidity or temperature within the measured ranges. Oysters were not observed to be impacted by any of the factors within measured ranges. Sponges had sustained abundance in half of the docks in this study. However, they did not respond to any of the environmental factors within measured ranges in different seasons. Results from this study can help target future Living Dock locations which will provide the best environment for the recruitment of filter feeding organisms. [ABSTRACT FROM AUTHOR]

The beta regression model (BRM) is used when the dependent variable may take continuous values and be bounded in the interval (0, 1), such as rates, proportions, percentages and fractions. Generally, the parameters of the BRM are estimated by the method of maximum likelihood estimation (MLE). However, the MLE does not offer accurate and reliable estimates when the explanatory variables in the BRM are correlated. To solve this problem, the ridge and Liu estimators for the BRM were proposed by different authors. In the current study, the James Stein Estimator (JSE) for the BRM is proposed. The matrix mean squared error (MSE) and the scalar MSE properties are derived and then compared to the available ridge estimator, Liu estimator and MLE. The performance of the proposed estimator is evaluated by conducting a simulation experiment and analyzing two real-life applications. The MSE of the estimators is considered as a performance evaluation criterion. The findings of the simulation experiment and applications indicate the superiority of the suggested estimator over the competitive estimators for estimating the parameters of the BRM. [ABSTRACT FROM AUTHOR]

Brazil was one of the countries most impacted by the COVID-19 pandemic, with a cumulative total of nearly 700,000 deaths by early 2023. The country's federative units were unevenly affected by the pandemic and adopted mitigation measures of different scopes and intensity. There was intense conflict between the federal government and state governments over the relevance and extent of such measures. We build a simple regression model with good predictive power on state COVID-19 mortality rates in Brazil. Our results reveal that the federative units' urbanization rate and per capita income are important for determining their mean mortality rate and that the number of physicians per 100,000 inhabitants is important for modeling the mortality rate precision. Based on the fitted model, we obtain approximations for the levels of administrative efficiency of local governments in dealing with the pandemic. [ABSTRACT FROM AUTHOR]

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended.

This paper examines the contribution of the consumption share to GDP from 1970 to 2019 to assess how income groups contributed to the share's stability, as well as the factors influencing its path and policy implications. Because the dependent variable is expressed in percentages and is not normally distributed, the Beta regression finite mixed model is the best tool for detecting latent income groups. We used Robust standard error to ensure the stability of the consumption share (i.e. the average propensity to consume APC). The analytical tool predicts the discovery of three previously unknown income groups. The following three latent groups were identified by the group means following the analysis: 0.82, 0.87, and 0.90 represent low-, medium-, and high-income groups, respectively, with probabilities of 0.27, 0.47, and 0.26 for their share contribution. The explanatory variables include employment, human capital (hc), total factor productivity at constant national prices (rtfpna), and the share of labor compensation in GDP. Each estimated parameter has a high significance and the expected sign. The policy implication is that the consumption share should be reduced in favor of the saving share by increasing employment opportunities and total factor productivity because it is relatively high. Furthermore, fighting inflation increases consumption while increasing spending on health care, education, and training stimulates economic growth by increasing the percentage saving ratio but at different levels. [ABSTRACT FROM AUTHOR]

In this paper we explore spatial patterns in voting behavior of the Brazilian electorate in the state legislative elections of 2014. With data aggregated at the municipality level, we employ a Beta regression model augmented with spatially correlated random effects to model the share of votes received by the three largest political parties: PMDB, PSDB, and PT. Results suggest PT is more preferred by the electorate in poorer and more densely populated areas, specially in states of the Northwest and South regions, while PSDB performs better in municipalities with relatively higher standards of living. Also, analysis of the spatial random effects indicates this component is especially important to account for the major stylized fact that is the simultaneous PSDB hegemony and relative lack of PMDB presence in the state of São Paulo. [ABSTRACT FROM AUTHOR]

The study was conducted to evaluate the effect of khat cultivation on rural households' income in the Bahir Dar Zuria district using cross-sectional data collected from 180 randomly selected respondents, and supported by focus group discussions in two districts of northwest Ethiopia. The data were analyzed by simple descriptive statistics and beta regression. Results from descriptive statistics show that khat contributes the largest (51%) of farmers' income, followed by crop sale (33%), sale of livestock and their products (9%), and off-farm and non-farm activities (7%), Empirical findings from the Beta regression model also show that farming experience, education status, the proportion of land allocated for khat cultivation, total working capital of the household, the density of khat trees planted per hectare, and participation in off-farm and non-farm activity have a significant and positive effect on the proportion of khat income of the households. On the contrary, livestock holding, total asset ownership, and access to mobile phones have a significant and negative influence on the proportion of annual khat income of the households. Hence, the cultivation of khat can have a significant effect on the improvement of rural households' income and standard of living in the districts. However, increased khat production have also serious implications on the market, water resources, and human health. Thus, policymakers need to come up together to understand and devise proper running mechanisms for these controversies of khat production in association with economic, social, and health implications. [ABSTRACT FROM AUTHOR]

The financing of long-term care and the planning of care capacity are of increasing interest due to demographic changes and the ageing population in many countries. Since many care-intensive conditions begin to manifest at higher ages, a better understanding and assessment of the expected costs, required infrastructure, and number of qualified personnel are essential. To evaluate the overall burden of institutional care, we derive a model based on the duration of stay in dependence and the intensity of help provided to elderly individuals. This article aims to model both aspects using novel longitudinal data from nursing homes in the canton of Geneva in Switzerland. Our data contain comprehensive health and care information, including medical diagnoses, levels of dependence, and physical and psychological impairments on 21,758 individuals. We build an accelerated failure time model to study the influence of selected factors on the duration of care and a beta regression model to describe the intensity of care. We show that apart from age and gender, the duration of stay before death is mainly affected by the underlying diseases and the number of different diagnoses. Simultaneously, care intensity is driven by the individual level of dependence and specific limitations. Using both evaluations, we approximate the overall care severity for individual profiles. Our study sheds light on the relevant medical, physical, and psychological health indicators that need to be accounted for, not only by care providers but also by policy-makers and insurers. [ABSTRACT FROM AUTHOR]