Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

Regression games

The solution of a TU cooperative game can be a distribution of the value of the grand coalition, i.e. it can be a distribution of the payo (utility) all the players together achieve. In a regression model, the evaluation of the explanatory variables can be a distribution of the overall t, i.e. the t of the model every regressor variable is involved. Furthermore, we can take regression models as TU cooperative games where the explanatory (regressor) variables are the players. In this paper we introduce the class of regression games, characterize it and apply the Shapley value to evaluating the explanatory variables in regression models. In order to support our approach we consider Young (1985)'s axiomatization of the Shapley value, and conclude that the Shapley value is a reasonable tool to evaluate the explanatory variables of regression models.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.

Development of operational performance models for truck-lane restrictions on freeway corridors

Highways are generally designed to serve a mixed traffic flow that consists of passenger cars, trucks, buses, recreational vehicles, etc. The fact that the impacts of these different vehicle types are not uniform creates problems in highway operations and safety. A common approach to reducing the impacts of truck traffic on freeways has been to restrict trucks to certain lane(s) to minimize the interaction between trucks and other vehicles and to compensate for their differences in operational characteristics. ^ The performance of different truck lane restriction alternatives differs under different traffic and geometric conditions. Thus, a good estimate of the operational performance of different truck lane restriction alternatives under prevailing conditions is needed to help make informed decisions on truck lane restriction alternatives. This study develops operational performance models that can be applied to help identify the most operationally efficient truck lane restriction alternative on a freeway under prevailing conditions. The operational performance measures examined in this study include average speed, throughput, speed difference, and lane changes. Prevailing conditions include number of lanes, interchange density, free-flow speeds, volumes, truck percentages, and ramp volumes. ^ Recognizing the difficulty of collecting sufficient data for an empirical modeling procedure that involves a high number of variables, the simulation approach was used to estimate the performance values for various truck lane restriction alternatives under various scenarios. Both the CORSIM and VISSIM simulation models were examined for their ability to model truck lane restrictions. Due to a major problem found in the CORSIM model for truck lane modeling, the VISSIM model was adopted as the simulator for this study. ^ The VISSIM model was calibrated mainly to replicate the capacity given in the 2000 Highway Capacity Manual (HCM) for various free-flow speeds under the ideal basic freeway section conditions. Non-linear regression models for average speed, throughput, average number of lane changes, and speed difference between the lane groups were developed. Based on the performance models developed, a simple decision procedure was recommended to select the desired truck lane restriction alternative for prevailing conditions. ^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

A study of moderating influences of adolescent alcohol drinking conditioned by perception of peer alcohol drinking

Despite a long history of prevention efforts and federal laws prohibiting the consumption of alcohol for those below the age of 21 years, underage drinking continues at both a high prevalence rate and high incidence rate. The purpose of this research study is to explain underage drinking of alcohol conditioned by perception of peer drinking. An acquisition model is conjectured and then a relationship within the model is explained with a national sample of students. From a developmental perspective, drinking alcohol is acquired in a reasonably ordered fashion that reflects the influences over time of the culture, family, and peers. The study measures perceptions of alcohol drinking during early adolescence when alcohol use begins the maintenance phase of the behavior. The correlation between drinking alcohol and perception of classmate drinking can be described via social learning theory. Simultaneously the moderating effects of grade level, gender, and race/ethnicity are used to explain differences between groups. Multilevel logistic regression was used to analyze the relations. The researcher found support for an association between adolescent drinking and perceptions of classmate drinking. Gender and grade level moderated the relation. African-Americans consistently demonstrated less drinking and less perception of classmate drinking than either whites or other students not white nor African-American. The importance of a better understanding of the process of acquiring drinking behaviors is discussed in relation to future research models with longitudinal data. ^

Financial aid and the persistence of associate of arts graduates transferring to a senior university

This study examined the effects of financial aid on the persistence of associate of arts graduates transferring to a senior university in one of four consecutive fall semesters (1998-2001). Situated in an international metropolitan area in the southeastern United States, the institution where the study was conducted is a large public research university identified as a Hispanic Serving Institution. Archival databases served as the source of information on the academic and social background of the 4,669 participants in the study. Data from institutional financial aid records were pooled with the data in the student administrative system.^ For purposes of this study, persistence was defined as ongoing progress until completing the baccalaureate degree. Student social background variables used in the study were gender, ethnicity, age, and income, with GPA and part-time or full-time enrollment status being the academic variables. Amount and type of aid, including grants, loans, scholarships, and work study were incorporated in the models to determine the effect of financial aid on the persistence of these transfer students. Because the dependent variable persistence had three possible outcomes (graduated, still enrolled, dropped out) multinomial logistic regression was the appropriate technique for analyzing the data; four multinomial models were employed in the analysis.^ Findings suggest that grants awarded based on the financial need of students and loans were effective in encouraging the persistence of students, but scholarships and work study were not effective.^

Effects of student-faculty interactions on persistence of underprepared community college students

The high concentration of underprepared students in community colleges presents a challenge to educators, policy-makers, and researchers. All have pointed to low completion rates and caution that institutional practices and policy ought to focus on improving retention and graduation rates. However, a multitude of inhibiting factors limits the educational opportunities of underprepared community college students. Using Tinto's (1993) and Astin's (1999) models of student departure as the primary theoretical framework, as well as faculty mentoring as a strategy to impact student performance and retention, the purpose of this study was to determine whether a mentoring program designed to promote greater student-faculty interactions with underprepared community college students is predictive of higher retention for such students. While many studies have documented the positive effects of faculty mentoring with 4-year university students, very few have examined faculty mentoring with underprepared community college students (Campbell and Campbell, 1997; Nora & Crisp, 2007). In this study, the content of student-faculty interactions captured during the mentoring experience was operationalized into eight domains. Faculty members used a log to record their interactions with students. During interactions they tried to help students develop study skills, set goals, and manage their time. They also provided counseling, gave encouragement, nurtured confidence, secured financial aid/grants/scholarships, and helped students navigate their first semester at college. Logistic regression results showed that both frequency and content of faculty interactions were important predictors of retention. Students with high levels of faculty interactions in the area of educational planning and personal/family concerns were more likely to persist. Those with high levels of interactions in time-management and academic concerns were less likely to persist. Interactions that focused on students' poor grades, unpreparedness for class, or excessive absences were predictive of dropping out. Those that focused on developing a program of study, creating a road map to completion, or students' self-perceptions, feelings of self-efficacy, and personal control were predictive of persistence.

Predicting hurricane evacuation decisions: When, how many, and how far

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. ^ Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. ^ Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. ^ All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. ^

Distribution of Diatoms and Development of Diatom-Based Models for Inferring Salinity and Nutrient Concentrations in Florida Bay and Adjacent Coastal Wetlands of South Florida (USA)

The composition and distribution of diatom algae inhabiting estuaries and coasts of the subtropical Americas are poorly documented, especially relative to the central role diatoms play in coastal food webs and to their potential utility as sentinels of environmental change in these threatened ecosystems. Here, we document the distribution of diatoms among the diverse habitat types and long environmental gradients represented by the shallow topographic relief of the South Florida, USA, coastline. A total of 592 species were encountered from 38 freshwater, mangrove, and marine locations in the Everglades wetland and Florida Bay during two seasonal collections, with the highest diversity occurring at sites of high salinity and low water column organic carbon concentration (WTOC). Freshwater, mangrove, and estuarine assemblages were compositionally distinct, but seasonal differences were only detected in mangrove and estuarine sites where solute concentration differed greatly between wet and dry seasons. Epiphytic, planktonic, and sediment assemblages were compositionally similar, implying a high degree of mixing along the shallow, tidal, and storm-prone coast. The relationships between diatom taxa and salinity, water total phosphorus (WTP), water total nitrogen (WTN), and WTOC concentrations were determined and incorporated into weighted averaging partial least squares regression models. Salinity was the most influential variable, resulting in a highly predictive model (r apparent 2  = 0.97, r jackknife 2  = 0.95) that can be used in the future to infer changes in coastal freshwater delivery or sea-level rise in South Florida and compositionally similar environments. Models predicting WTN (r apparent 2  = 0.75, r jackknife 2  = 0.46), WTP (r apparent 2  = 0.75, r jackknife 2  = 0.49), and WTOC (r apparent 2  = 0.79, r jackknife 2  = 0.57) were also strong, suggesting that diatoms can provide reliable inferences of changes in solute delivery to the coastal ecosystem.

The relationship of aptitude, course grades, and program length, to performance on a standards based test (NCLEX -RN)

The purpose of this study was threefold: first, to investigate variables associated with learning, and performance as measured by the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The second purpose was to validate the predictive value of the Assessment Technologies Institute (ATI) achievement exit exam, and lastly, to provide a model that could be used to predict performance on the NCLEX-RN, with implications for admission and curriculum development. The study was based on school learning theory, which implies that acquisition in school learning is a function of aptitude (pre-admission measures), opportunity to learn, and quality of instruction (program measures). Data utilized were from 298 graduates of an associate degree nursing program in the Southeastern United States. Of the 298 graduates, 142 were Hispanic, 87 were Black, non-Hispanic, 54 White, non-Hispanic, and 15 reported as Others. The graduates took the NCLEX-RN for the first time during the years 2003–2005. This study was a predictive, correlational design that relied upon retrospective data. Point biserial correlations, and chi-square analyses were used to investigate relationships between 19 selected predictor variables and the dichotomous criterion variable, NCLEX-RN. The correlation and chi square findings indicated that men did better on the NCLEX-RN than women; Blacks had the highest failure rates, followed by Hispanics; older students were more likely to pass the exam than younger students; and students who passed the exam started and completed the nursing program with a higher grade point average, than those who failed the exam. Using logistic regression, five statistical models that used variables associated with learning and student performance on the NCLEX-RN were tested with a model adapted from Bloom's (1976) and Carroll's (1963) school learning theories. The derived model included: NCLEX-RNsuccess = f (Nurse Entrance Test and advanced medical-surgical nursing course grade achieved). The model demonstrates that student performance on the NCLEX-RN can be predicted by one pre-admission measure, and a program measure. The Assessment Technologies Institute achievement exit exam (an outcome measure) had no predictive value for student performance on the NCLEX-RN. The model developed accurately predicted 94% of the student's successful performance on the NCLEX-RN.

Effects of student-faculty interactions on persistence of underprepared community college students.

The high concentration of underprepared students in community colleges presents a challenge to educators, policy-makers, and researchers. All have pointed to low completion rates and caution that institutional practices and policy ought to focus on improving retention and graduation rates. However, a multitude of inhibiting factors limits the educational opportunities of underprepared community college students. Using Tinto's (1993) and Astin's (1999) models of student departure as the primary theoretical framework, as well as faculty mentoring as a strategy to impact student performance and retention, the purpose of this study was to determine whether a mentoring program designed to promote greater student-faculty interactions with underprepared community college students is predictive of higher retention for such students. While many studies have documented the positive effects of faculty mentoring with 4-year university students, very few have examined faculty mentoring with underprepared community college students (Campbell and Campbell, 1997; Nora & Crisp, 2007). In this study, the content of student-faculty interactions captured during the mentoring experience was operationalized into eight domains. Faculty members used a log to record their interactions with students. During interactions they tried to help students develop study skills, set goals, and manage their time. They also provided counseling, gave encouragement, nurtured confidence, secured financial aid/grants/scholarships, and helped students navigate their first semester at college. Logistic regression results showed that both frequency and content of faculty interactions were important predictors of retention. Students with high levels of faculty interactions in the area of educational planning and personal/family concerns were more likely to persist. Those with high levels of interactions in time-management and academic concerns were less likely to persist. Interactions that focused on students' poor grades, unpreparedness for class, or excessive absences were predictive of dropping out. Those that focused on developing a program of study, creating a road map to completion, or students' self-perceptions, feelings of self-efficacy, and personal control were predictive of persistence.

Modelos de dispersão para extremos de precipitação. Estudo de caso: o nordeste do Brasil

In this thesis used four different methods in order to diagnose the precipitation extremes on Northeastern Brazil (NEB): Generalized Linear Model s via logistic regression and Poisson, extreme value theory analysis via generalized extre me value (GEV) and generalized Pareto (GPD) distributions and Vectorial Generalized Linea r Models via GEV (MVLG GEV). The logistic regression and Poisson models were used to identify the interactions between the precipitation extremes and other variables based on the odds ratios and relative risks. It was found that the outgoing longwave radiation was the indicator variable for the occurrence of extreme precipitation on eastern, northern and semi arid NEB, and the relative humidity was verified on southern NEB. The GEV and GPD distribut ions (based on the 95th percentile) showed that the location and scale parameters were presented the maximum on the eastern and northern coast NEB, the GEV verified a maximum core on western of Pernambuco influenced by weather systems and topography. The GEV and GPD shape parameter, for most regions the data fitted by Weibull negative an d Beta distributions (ξ < 0) , respectively. The levels and return periods of GEV (GPD) on north ern Maranhão (centerrn of Bahia) may occur at least an extreme precipitation event excee ding over of 160.9 mm /day (192.3 mm / day) on next 30 years. The MVLG GEV model found tha t the zonal and meridional wind components, evaporation and Atlantic and Pacific se a surface temperature boost the precipitation extremes. The GEV parameters show the following results: a) location ( ), the highest value was 88.26 ± 6.42 mm on northern Maran hão; b) scale ( σ ), most regions showed positive values, except on southern of Maranhão; an d c) shape ( ξ ), most of the selected regions were adjusted by the Weibull negative distr ibution ( ξ < 0 ). The southern Maranhão and southern Bahia have greater accuracy. The level period, it was estimated that the centern of Bahia may occur at least an extreme precipitatio n event equal to or exceeding over 571.2 mm/day on next 30 years.