Linear Regression-based Model Research Articles

Estimating discretionary accruals using a grouping genetic algorithm

A number of different models have been suggested for detecting earnings management but the linear regression-based model presented by Jones (1991) is the most frequently used. The underlying assumption with the Jones model is that earnings are managed through accounting accruals. Typically, the companies for which earnings management is studied are grouped based on their industries. It is thus assumed that the accrual generating process for companies within a specific industry is similar. However, some studies have recently shown that this assumption does not necessarily hold. An alternative approach which returns a grouping which is, if not optimal, at least very close to optimal is the use of genetic algorithms. The purpose of this study is to assess the performance of the cross-sectional Jones accrual model when the data set firms are grouped using a grouping genetic algorithm. The results provide strong evidence that the grouping genetic algorithm method outperforms the various alternative grouping methods.

Detecting earnings management with neural networks

A large body of studies has examined the occurrence of earnings management in various contexts. In most studies, the assumption has been that earnings are managed through accounting accruals. Thus, a range of accrual based earnings management detection models have been suggested. The ability of these models to detect earnings management has, however, been questioned in a number of studies. An explanation to the poor performance of the existing models is that most models use a linear approach for modeling the accrual process even though the accrual process has in fact proven non-linear in several studies. An alternative way to deal with the non-linearity is to use various types of neural networks. The purpose of this study is to assess whether neural network-based models outperform linear and piecewise linear-based models in detecting earnings management. The study comprises neural network models based on a self-organizing map (SOM), a multilayer perceptron (MLP) and a general regression neural network (GRNN). The results show that the GRNN-based model performs best, whereas the linear regression-based model has the poorest performance. However, the results also show that all five models assessed in this study estimate discretionary accruals, a proxy for earnings management, with some bias.

Response of the water balance in Greece to temperature and precipitation trends

This paper deals with the most recent trends in meteorological and hydrological variables, which include air temperature and precipitation (P), potential and actual (ET) evapotranspiration, surface runoff (RO), water recharge into the soil (R) and water loss from the soil (L). Most hydrological variables were calculated via Palmer's algorithm. For this purpose, two rank-based statistical tests (the Mann–Kendall (MK) and a change-point analysis (CPA) approach) and the basic linear regression-based model were applied on the weekly precipitation and temperature from 17 stations all over Greece, during 1961–2006. Only in winter, all variables except for R, which showed no clear signal, presented downward trends. The declining trends of P and L in spring and summer were counterbalanced by reductions in RO (and R in the case of summer) as opposed to increases in ET. In autumn, the declining tendencies of P and L were offset by RO reductions and R increases. Annually, the trends in water cycle components were analogous to that of spring, summer and autumn. The number of stations with statistically significant (at 95%) trends greatly varied with season and meteorological/hydrological variable.

Modeling the Association between 43 Different Clinical and Pathological Variables and the Severity of Cognitive Impairment in a Large Autopsy Cohort of Elderly Persons

We evaluated the association between mini-mental status examination (MMSE) scores proximal to death and the values of 43 different clinical and pathological parameters. Studies were performed using data from 334 elderly, longitudinally evaluated research subjects who had undergone autopsy and satisfied inclusion criteria from an initial study group of 501. Interindividual variance in MMSE scores was used as a surrogate for the severity of cognitive impairment linked to aging (CILA). A statistical linear regression-based model provided a framework for assessing the parameters with significant, direct impact on CILA severity. Strong association between CILA and Alzheimer's disease (AD) pathology, especially isocortical neurofibrillary tangles, was evident. The pattern of association between AD lesion densities with cognitive impairment severity was biologically informative, with neuritic plaques having more impact in relatively high-functioning individuals. Abundant isocortical Lewy bodies tended to be an additive pathology correlating with final MMSE scores approximately 10 points lower. In a subset of cases we found evidence for association between TDP-43-related pathology and CILA severity, independent of AD or hippocampal sclerosis. There was no support for independent association between CILA severity and most evaluated indices including diffuse plaques, argyrophilic grains, heart disease, education level, apolipoprotein E alleles or diabetes.

Rapid evaluation of poultry manure content using artificial neural networks (ANNs) method

With increasing concern over the potential pollution from farm wastes, there is a need for rapid and robust methods that can analyse animal manure. In order to evaluate rapid testing methods based on the relationship between layer manure composition (ammonium nitrogen, total potassium, total nitrogen, total phosphorus, iron, copper, zinc, magnesium and sodium) and physicochemical properties (specific gravity, electrical conductivity, pH), diverse layer manure samples (n = 105) were used. Relationships were investigated using linear regression and artificial neural networks (ANNs). The performance of a neural network-based model was compared with a linear regression-based model using the same observed data. It was found that ANN model consistently gives better predictions. Based on the results of this study, ANNs appear to be a promising technique for predicting layer manure composition.

A regression-based approach to short-term system load forecasting

The authors describe a novel linear regression-based model for the calculation of short-term system load forecasts. The model's most significant new aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables; temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; the use of 'reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. The significant impact of these issues on the accuracy of a model's results was established through testing of an existing load forecasting algorithm. The model has been tested under a variety of conditions and it was shown to produce excellent results. It is also sufficiently general to be used by other electric power utilities. >