Classical Multiple Regression Model Research Articles

Prediction of cyber-attacks in air transport using neural networks

This article addresses the topic of cyber-attacks in air transport, which is crucial for ensuring the safety and reliability of airports and air transport operations. The aim of the article was to present a new method for predicting cyber-attacks in air transport based on neural networks. The task of the neural network was to determine the multiple regression function based on which the probability of a cyberattack occurring at a specified hour and on a specific day of the week is predicted. The probability, depending on the time of the cyberattack occurrence, was determined using theoretical distributions. The method was verified with real data. Verification of the method confirmed its high effectiveness, determined at the level of 92%. The study examined the effectiveness of using the classical multiple regression method in predicting cyber-attacks in air transport. The classical multiple regression model covered only 0.14 of the input data, while the regression model generated by the neural network covered 0.99, indicating the high efficiency of the developed neural network.

Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model.

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald–Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

Intervention analysis of Nigeria’s foreign exchange rate

This paper investigated the impact of Nigeria’s foreign exchange rate using classical multiple regression model under the assumptions of ordinary least squares method (OLS) and intervention model using lag operator L. Monthly time series data spanning 1980:1 to 2014:12 were used and a number of statistical tools are employed to verify this hypothesis. A useful approach is to test the significant change between the long-run mean effect before and after each intervention. Akaike's information criterion (AIC), Schwarz’s Bayesian criterion (SBC or BIC) and Coefficient of Determination (R2) were used to determine the model that best describe Nigeria’s foreign exchange rate.Keywords: Intervention Analysis, Exchange Rate

Big data analytics: integrating penalty strategies

We present efficient estimation and prediction strategies for the classical multiple regression model when the dimensions of the parameters are larger than the number of observations. These strategies are motivated by penalty estimation and Stein-type estimation procedures. More specifically, we consider the estimation of regression parameters in sparse linear models when some of the predictors may have a very weak influence on the response of interest. In a high-dimensional situation, a number of existing variable selection techniques exists. However, they yield different subset models and may have different numbers of predictors. Generally speaking, the least absolute shrinkage and selection operator (Lasso) approach produces an over-fitted model compared with its competitors, namely the smoothly clipped absolute deviation (SCAD) method and adaptive Lasso (aLasso). Thus, prediction based only on a submodel selected by such methods will be subject to selection bias. In order to minimize the inherited bias, we suggest combining two models to improve the estimation and prediction performance. In the context of two competing models where one model includes more predictors than the other based on relatively aggressive variable selection strategies, we plan to investigate the relative performance of Stein-type shrinkage and penalty estimators. The shrinkage estimator improves the prediction performance of submodels significantly selected from existing Lasso-type variable selection methods. A Monte Carlo simulation study is carried out using the relative mean squared error (RMSE) criterion to appraise the performance of the listed estimators. The proposed strategy is applied to the analysis of several real high-dimensional data sets.

Modelo de regressão espacial para estimativa da produtividade da soja associada a variáveis agrometeorológicas na região oeste do estado do Paraná

Este trabalho apresenta o Modelo de Regressão Espacial Autorregressivo Misto (SAR) e Modelo do Erro Espacial (CAR) no intuito de investigar a associação entre a produtividade da soja e as variáveis agrometeorológicas relacionadas à precipitação pluvial, temperatura média e radiação solar global. O estudo foi realizado com os dados das safras dos anos agrícolas de 2005/2006 a 2007/2008, da região oeste do estado do Paraná. Como os dados agrometeorológicos estão disponíveis apenas para oito municípios da região em estudo, as estimativas foram obtidas por meio do uso de Polígonos de Thiessen. A estimativa de parâmetros dos modelos ajustados foi obtida utilizando o método de Máxima Verossimilhança. A avaliação do desempenho dos modelos foi realizada com base no coeficiente de determinação (R²), no máximo valor do logaritmo da função verossimilhança e no critério de informação bayesiano de Schwarz (BIC). Este estudo também permitiu verificar a correlação e autocorrelação espacial entre a produtividade da soja e os elementos agrometeorológicos, por meio da análise espacial de área, usando de técnicas como o índice I de Moran Global e Local uni e bivariado, e os testes de significância. O estudo pôde demonstrar que, por meio dos indicadores de desempenho utilizados, os modelos SAR e CAR ofereceram melhores resultados em relação ao modelo de regressão múltipla clássica.

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.

Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates

An existing dataset of area-specific sediment yield (SSY) for 60 catchments in Spain that was retrieved from sediment deposition rates in reservoirs [Avendaño Salas, C., Sanz Montero, E., Rayán, C., Gómez Montaña, 1997. Sediment yield at Spanish reservoirs and its relationship with the drainage basin area. In: Proceedings of the 19th Symposium of Large Dams, Florence, 1997. ICOLD (International Committee on Large Dams), pp. 863–874] reveals that catchment area alone explains only 17% of the variability in SSY. In this study, an attempt to explain the remaining variability in SSY was made using a quantitative and a semiqualitative approach for 22 catchments. During a field survey, the 22 selected catchments were characterised by topography, vegetation cover, lithology, shape and the presence of gullies in the broad vicinity of the reservoir. This information was used to develop a factorial scoring index model that provides a fairly accurate and reliable prediction of SSY. A classical multiple regression model using climatic, topographic and land use properties derived from regional datasets could not explain as much variance as the qualitative index model, nor did it appear to be as reliable. The same conclusion could be drawn when using the CORINE soil erosion risk map of southern Europe. The low prediction capability of the multiple regression models and the CORINE soil erosion risk map could be attributed mainly to the fact that these methods do not incorporate gully erosion and that the land cover data are not a good representation of soil cover. Both variables have been shown to be of great importance during the field surveys. Future assessments of SSY could be quickly and efficiently made using the proposed factorial scoring index model. In comparison with other models, which demand more data, the index model offers an alternative prediction tool.