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The paper considers the problem of nonparametric estimation of nonlinear characteristic in the FIR Wiener system. Methods proposed so far have suffered from the so-called 'curse of dimensionality' and their practical applications have been limited to the very short impulse responses of the linear component. In the proposed approach, the class of (stochastically initialised) repeated exponential input excitations is introduced, and a modified kernel-based estimator is presented. It is shown that the rate of convergence to the genuine system characteristic is significantly improved and does not depend on the length of the impulse response of the linear dynamic block. [ABSTRACT FROM AUTHOR]

In this paper, a novel ensemble learning model named EWT-EnsemLSTM-SSA, which assembles long short-term memory (LSTM), support vector regression (SVR), and sparrow search algorithm (SSA), is a proposed to deal with long term metro passenger flow volume prediction, which is an essential content of traffic flow prediction problems. Firstly, the empirical wavelet transform (EWT) method is introduced to decompose the original dataset into five wavelet time-sequence data for further prediction. Then, a cluster of LSTMs with diverse hidden layers and neuron counts are employed to explore and exploit the implicit information of the EWT-decomposed signals. Next, the output of LSTMs is aggregated into a nonlinear regression method SVR. Lastly, SSA is utilized to optimize the SVR automatically. The proposed EWT-EnsemLSTM-SSA model is applied in three case studies, using the data collected from the passengers' amount in the Minneapolis, America metro, divided into one hour in one day. Experiment results, which compare the proposed EnsemLSTM-SSA model with five conventional time series forecasting models, show that the proposed model can achieve a better performance than the traditional prediction algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose and assess the accuracy of new fuel consumption estimation models for vehicle routing. Based on real-world data consisting of instantaneous fuel consumption, time-varying speeds observations, and high-frequency traffic, we propose effective methods to estimate fuel consumption. By carrying out nonlinear regression analysis using supervised learning methods, namely Neural Networks, Support Vector Machines, Conditional Inference Trees, and Gradient Boosting Machines, we develop new models that provide better prediction accuracy than classical models. We correctly estimate consumption for time-dependent point-to-point routing under realistic conditions. Our methods provide a more precise alternative to classical regression methods used in the literature, as they are developed for a specific situation. Extensive computational experiments under realistic conditions show the effectiveness of the proposed machine learning consumption models, clearly outperforming macroscopic and microscopic consumption models such as the Comprehensive Modal Emissions Model (CMEM) and the Methodology for Estimating air pollutant Emissions from Transport (MEET). Based on sensitivity analyses we show that MEET underestimates real-world consumption by 24.94% and CMEM leads to an overestimation of consumption by 7.57% with optimised parameters. Our best machine learning model (Gradient Boosting Machines) exhibited superior estimation accuracy with a gap of only 1.70%. [ABSTRACT FROM AUTHOR]

Public departments of transportation (DOTs) are increasingly adopting alternative contracting methods (ACMs), such as Indefinite Delivery/Indefinite Quantity (IDIQ) agreements, to shorten project delivery and increase scheduling flexibility. However, the challenge remains to decide when an IDIQ contract would offer better value for money in a project compared to the conventional Design-Bid-Build (DBB) method. This paper proposes an IDIQ project selection framework that incorporates expected construction costs to identify suitable candidate projects. Historical bid data, an alternate cost indexing system, nonlinear regression analysis, and the Monte Carlo simulation technique are used to create and compare IDIQ and DBB probabilistic construction cost estimates. The study demonstrates the effectiveness of the framework using a single-award IDIQ granted through the Minnesota Department of Transportation (MnDOT) and past bidding information for projects awarded between January 2008 and April 2015. Additionally, the paper acknowledges the limitations of the study, including its reliance on MnDOT data, the exclusion of non-cost factors, and the assumption of project homogeneity. The study also provides valuable recommendations for future research to address these limitations and enhance the understanding and practical implementation of IDIQ contracting in the construction industry. [ABSTRACT FROM AUTHOR]

Gender poverty differences in households are likely to affect female-headed households more than male-headed households. This paper examined the evolution of the gender poverty rate gap and identified the factors that underlie differences in poverty rates between female-headed households and male-headed households using the most recent representative household surveys conducted by the Kenya National Bureau of Statistics in 2005/06 and 2015/16. An extended Blinder-Oaxaca decomposition analysis with nonlinear regression was performed. The findings indicate that poverty rates for female-headed households and male-headed households declined from 38.56 to 32.73% in 2005/06 to 30.23 and 26.04% in 2015/16, respectively. Although female-headed households (1.12) have a higher chance of falling into poverty than male-headed households (0.95), the decline in the poverty rate was higher for female-headed households (8.33%) than for male-headed households (6.69%). Therefore, the results do not support the feminization of poverty hypothesis in Kenya. Factors that have bridged the gender poverty gap include cash transfers that explain 11.02% of the gaps, literacy (53.97%), university education (10.39%), secondary education (40.84%), employment in public and private sectors (26.66%) and business employment (10.58%). Recommended policies include the implementation of the gender policy and affirmative action, enhancing literacy levels, and secondary and university enrolment. [ABSTRACT FROM AUTHOR]

AbstractData transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN. [ABSTRACT FROM AUTHOR]

AbstractThis paper was conducted to model the drying kinetics of Capparis spinosa buds using dragonfly swarm optimized nonlinear regression and to compare the impact of drying treatments on their quality. Experiments included hot-air convective drying (HACD) from 40 to 120 °C, vacuum drying (VD) at 40, 60, and 80 °C, microwave drying (MD) ranging from 200 to 1000 W. Out of 30 models tested, the drying kinetics fitted best with Jena Das model in both HACD and VD and modified Midilli for MD. The comparison between the drying treatments used indicated that when taking into account the quality of the dried caper, VD at 80 °C was the most effective, resulting in a high-quality caper with a well-preserved color (ΔE = 7.47), high bioactives (TPC = 30.18mgGAE/gDW and TFC = 10.27 mg QE/gDW) and radical scavenging abilities (DPPH = 0.249 and ABTS = 4.448 mg/mL) at the expense of long duration (6h) and high specific energy consumption (50.667 kWh/kg). On the other hand, when considering the drying behavior MD ranked best with samples dried at 1000 W showing high diffusivities 2.04e−8 m2s−1 low energy consumption (MER 0.1216 kg/h), and short drying times (12 mn). This research is critical in selecting better drying conditions for increased usage in the culinary, cosmetic, and pharmaceutical sectors. [ABSTRACT FROM AUTHOR]

This paper evaluates linear and nonlinear regression analysis to describe the empirical adsorption kinetics using pseudo-first-order (PFO) and pseudo-second-order (PSO) models. These models have been used to characterize the performance of adsorbents for environmental remediation and environmental modeling. Data were simulated using the PFO and PSO models with 1, 2, and 5% noise levels and fitted by nonlinear and linearized PFO and PSO equations. Nonlinear regression analysis provided rate constants and adsorption capacities with better accuracy than linearization. Besides the correlation coefficient, Chi-square and residual plot analysis helped choose the proper model to describe the adsorbent efficiency and validate the results. Both models and the NLR fitting were employed to reevaluate data obtained in our research group, including the adsorption of Hg(II) on thiol-modified vermiculite, glyphosate on soils rich in aluminum and iron oxides, phosphate on Fe(III) polyhydroxy cations modified montmorillonite, and paraquat on soil and vermiculite. While fitting the simulated data indicates an unequivocal and correct kinetic model, fitting the experimental data is not straightforward, suggesting mixed models rule the adsorption and that a large number of data points, especially at the initial steps of adsorption, provided by high throughput analysis, help to improve the kinetic modeling. [ABSTRACT FROM AUTHOR]

Building Performance Simulation (BPS), a useful tool to assess the operational performance of buildings and systems, can often be computationally expensive. The use of BPS is cumbersome for problems where the speed of response is important, e.g. real-time control, uncertainty quantification, parametric exploration, or stock modelling. Emulators, such as those based on regression, offer a faster substitute, but their reliability can be questionable. This paper proposes seven tests to check if an emulator is a suitable replacement for simulation in practice. The tests are categorized using four criteria: accuracy, speed, generalisability, and ease of use. The tests can be included in the process of setting up an emulator-based workflow. A use case is provided for emulators based on linear and non-linear regression (Gaussian Process models). This work aims to enable a practitioner to reliably conduct performance assessment for buildings using emulators. [ABSTRACT FROM AUTHOR]

This study investigates the impact mechanism of the government's strategic leadership (SL) and environmental optimisation (EO) on the improvement of regional innovation performance (RIP) under the effects of the regional innovation system coupling synergy degree (RISCSD). Based on the nonlinear threshold regression model, the coupling synergy model and entropy value method, the threshold regression analysis is conducted by calculating the RISCSD. The results indicate that: under the effect of the RISCSD, the SL, EO, and RIP have distinct single and double threshold effects. With the improvement of RISCSD, the influence of SL on RIP has changed from inhibition to promotion; while the promotion effect of EO on RIP shows an upward trend at first, and then slightly weakening. This paper provides relevant policy suggestions for local public administrations to enhance RIP. [ABSTRACT FROM AUTHOR]

The shear behaviour of fibre-reinforced polymer reinforced concrete (FRP-RC) beams without web reinforcement suffers from low strength, low stiffness, more brittleness and wide and quick propagated cracks. Fortunately, the addition of various types of fibres could improve most of these weaknesses. On the other hand, the shear strength prediction of FRP-RC beams with various types of fibres is one of the most complex cases in structural engineering applications. Developing generalised, precise and consistent prediction models are necessary and very limited. This paper investigates the impacts of various types of fibres on shear strength and presents proposing four new prediction models, utilising artificial neural networks and empirical nonlinear regression analysis, and modifying the combination of available models based on a collected database of 49 shear test results of FRP-RC members with various types of fibres. The comparison of the developed models with the available equations from the literature indicates that the developed models yielded excellent performance, great efficiency and a high level of accuracy over all other existing models. Additionally, the parametric study confirmed that all the developed models have great abilities to accurately predict the actual response of each parameter, in spite of its complexity, on the shear strength of FRP-reinforced fibrous concrete beams without stirrups. [ABSTRACT FROM AUTHOR]

The consistency of model selection criterion BIC has been well and widely studied for many nonlinear regression models. However, few of them had considered models with lag variables as regressors and auto-correlated errors in time series settings, which is common in both linear and nonlinear time series modeling. This paper studies a dynamic semi-varying coefficient model with ARMA errors, using an approach based on spectrum analysis of time series. The consistency property of the proposed model selection criteria is established and an implementation procedure of model selection is proposed for practitioners. Simulation studies have also been conducted to numerically show the consistency property. [ABSTRACT FROM AUTHOR]

This paper considers nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with exponential parametric distortion measurement errors. To estimate parameters in the distortion functions, we propose nonlinear least squares and weighted nonlinear least squares estimation methods under two identifiability conditions. After obtaining calibrated variables, the nonlinear least squares based estimators are proposed to estimate the parameters in the regression model. We studied the asymptotic results of estimators, especially we discuss the difference between the parametric calibrations and nonparametric calibrations. The latter is conducted as if the parametric structures in distortion functions are unknown. Simulation studies demonstrate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

This paper addresses the design problem of outpatient chemotherapy oncology departments. The objective is to identify the most effective resource configuration (e.g. number of chairs, number of oncologists) to reduce the patient waiting time and increase the average number of daily treatments. A stochastic simulation model was developed to emulate the patient flow in different oncology unit configurations. Then, it was used to generate the expected results of an extended design of experiments. To evaluate the impact of the different resources on the performance of the oncology departments the ANOVA analysis was employed. Besides, a Pareto multi-objective analysis was carried out to further support any decision-maker in the selection of the most suitable resource configuration. Particularly, to make easier the decision-making process, a comprehensive table chart was elaborated. Finally, a multiple non-linear regression model was generated in order to empower the health managers in easily assessing the performance of any real ward configuration. The analysis of results pointed out that the two mentioned objectives are rather conflicting. Furthermore, it proved that a higher number of resources not necessarily implies a significant improvement in the performance measures. [ABSTRACT FROM AUTHOR]

Dewatering is considered as extremely significant and primary step to enhance the quality of lignite. A great number of research focus on conventional fluidized-bed drying technology or dynamics. In this paper, the microwave deep drying kinetics of Zhaotong lignite under different power levels and with different sample thicknesses were investigated. The drying kinetics of microwave deep drying was fitted by 13 different drying models given in the literature. Experimental results indicated that the microwave deep drying process exists three segments: increasing-rate, falling-rate and brief constant-rate segment. The drying time decreased and drying rate increased with microwave output power level increase, while the microwave penetration depth in lignite was affected by sample thickness, hence there is an irregular relationship between lignite sample thickness and microwave drying time or drying rate. Based on non-linear regression analysis, the Page model was considered adequate to describe the microwave deep drying behavior of Zhaotong lignite under various conditions, and the model could be used for residual moisture water content prediction at any time during the microwave deep drying process. Effective diffusion coefficient increased along with microwave output power level increase. The activation energy of the moisture emission from the lignite under different power levels was estimated at 10.2202 W/g using modified Arrhenius equation. The pore structure damage was observed in the lignite samples after microwave deep drying by SEM analysis. The surface of upgraded lignite had visible cracks, defects and small fragments compared with the samples by conventional drying. [ABSTRACT FROM AUTHOR]

The models used to describe the kinetics of ruminal degradation are usually nonlinear models where the dependent variable is the proportion of degraded food. The method of least squares is the standard approach used to estimate the unknown parameters but this method can lead to unacceptable predictions. To solve this issue, a beta nonlinear model and the Bayesian perspective is proposed in this article. The application of standard methodologies to obtain prior distributions, such as the Jeffreys prior or the reference priors, involves serious difficulties here because this model is a nonlinear non-normal regression model, and the constrained parameters appear in the log-likelihood function through the Gamma function. This paper proposes an objective method to obtain the prior distribution, which can be applied to other models with similar complexity, can be easily implemented in OpenBUGS, and solves the problem of unacceptable predictions. The model is generalized to a larger class of models. The methodology was applied to real data with three models that were compared using the Deviance Information Criterion and the root mean square prediction error. A simulation study was performed to evaluate the coverage of the credible intervals. [ABSTRACT FROM AUTHOR]

Although the exogeneity condition is usually used in many econometric models to identify parameters, the stronger restriction that the error term is independent of a vector of exogenous variables might lead to theoretical benefits. In this paper, we develop a unified methodology for testing the independence assumption. Our methodology can deal with a wide class of parametric models and allows for endogeneity and instrumental variables. In the first-step development, we construct tests that are continuous functionals of the estimated difference of the joint distribution and the product marginal distributions. Next, to remedy the dimensionality issue that arises when the dimension of the exogenous random vector is large, we propose a multiple testing approach which combines marginal p-values obtained by employing the original tests to test independence between the error term and each exogenous variable, while taking full account of the multiplicity nature of the testing problem. We obtain null limiting distributions of our tests, establish the testing consistency, and justify the sensitivity to n-1/2-local alternatives, with n the sample size. The multiplier bootstrap is employed to estimate the critical values. Our methodology is illustrated in the linear regression, the instrumental variables regression, and the nonlinear quantile regression. Our tests are found to perform well in simulations and are demonstrated via an empirical example. [ABSTRACT FROM AUTHOR]

This paper considers nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. We proposed the second order least squares estimator by including in the criterion function the distance of both the response variable to its conditional mean and the squared response variable to its second conditional moment given the predictor variables. The asymptotic results of the second order nonlinear least squares estimators for the regression parameter are derived. As a by-product, we construct confidence intervals by estimating the asymptotic covariance matrices. We also consider to use three existing calibration procedures to calibrate the unknown response and covariates, namely, the conditional absolute mean calibration, the conditional variance calibration and the logarithmic calibration. The simulation studies shows that these second order nonlinear least squares estimators with three calibration procedures are all superior than the ordinary nonlinear least squares estimators. [ABSTRACT FROM AUTHOR]

Neural networks have become one of the most popularly used methods in machine learning and artificial intelligence. Due to the universal approximation theorem, a neural network with one hidden layer can approximate any continuous function on compact support as long as the number of hidden units is sufficiently large. Statistically, a neural network can be classified into a nonlinear regression framework. However, if we consider it parametrically, due to the unidentifiability of the parameters, it is difficult to derive its asymptotic properties. Instead, we consider the estimation problem in a nonparametric regression framework and use the results from sieve estimation to establish the consistency, the rates of convergence and the asymptotic normality of the neural network estimators. We also illustrate the validity of the theories via simulations. [ABSTRACT FROM AUTHOR]

This paper studies the storage life of ICR18650 lithium-ion batteries at different temperatures and different SOC (stage of charge). The author establishes a storage life prediction model. Based on the empirical model, the author analyzed the relationship between temperature and SOC about the influencing factors through experimental data, and used nonlinear regression fitting to determine the model parameters, and the storage life prediction model of irreversible capacity loss with respect to temperature, SOC, and time is finally established. Based on the differences of monomers and measurement errors during test, an error model was established, and a Monte Carlo simulation test was conducted on the battery storage life. Then we build a SVR model based on the obtained data. According to the SVR model, we can analyze the health state of the battery pack based on the given parameters of battery pack in storage. Thus, we can make a prognosis about the SOH of battery pack quickly and accurately. The innovation of the research is that based on the storage test of battery cells combined with data analysis and deductive simulation, we can predict the health state of the whole battery pack. The operation can greatly save experimental resources. [ABSTRACT FROM AUTHOR]

In this paper, the synthesis of 2,4,6-trinitrophenol from phenol was improved in the range of 0–65°C, and the yield was 79.4%. The effectiveness of the synthesis process was confirmed by Nuclear Magnetic Resonance, Fourier Transform Infrared Spectrometer, and Scanning Electron Microscope. The thermal stability of the product was further studied by differential scanning calorimetry and accelerating rate calorimeter. The kinetic model established by combining linear regression and nonlinear regression methods shows that the apparent activation energy of the energetic material synthesized under the green process is about 130 kJ/mol. [ABSTRACT FROM AUTHOR]

This paper considers the efficient estimation for a parametric regression model. For parameters estimation, three estimation methods for the parameters are proposed. These estimators are the semi-parametric profile nonlinear least squares estimators, the nonlinear least squares estimators and one-step estimators. We study the asymptotic properties of the proposed estimators, and further discuss their estimation efficiency. The asymptotic normal confidence intervals and empirical likelihood confidence intervals are also proposed for parameters. Simulation studies are conducted to compare the proposed estimation methods. [ABSTRACT FROM AUTHOR]

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data. [ABSTRACT FROM AUTHOR]

This paper elucidates the outcome of machining factors specifically cutting velocity, feed rate and depth of cut on the machining characteristics such as surface roughness and tool-work interface temperature while turning cylindrical AISI 52100 steel alloy components in dry and microlubrication machining conditions. The experiments are designed based on the Taguchi's L25(53) orthogonal array and conducted on an All Geared Lathe, simultaneously the tool-work interface temperature was observed using a K-type tool-work thermocouple and subsequently, a mathematical model was developed for the tool-work interface temperature values through regression analysis. The surface roughness of the turned steel alloy components is deliberated by means of a precise surface roughness apparatus. A prediction model in lieu of average surface roughness is created by means of nonlinear regression examination with the aid of MINITAB software. The most favourable machining settings are recognised by Taguchi's method and verified with a confirmation trial. The results revealed that machining under microlubrication condition exhibited lesser temperature at the tool-work interface and the quality of the machined components also exhibits lesser roughness when compared with components of dry machining condition. [ABSTRACT FROM AUTHOR]

This paper describes the effect of stabilizer content, curing time and moisture content on the UCS based upon 150 samples of stabilized soil. The results indicate an optimum value of lime or cement content which corresponds to the maximum UCS. The optimum lime content is dependent to moisture content, while the optimum cement content is independent of it. The increase of the UCS values due to increase of the stabilizer content in cement-stabilized samples is noticeably higher than lime-stabilized ones. The UCS values have also a direct and inverse relationship respectively with curing time and moisture content. UCS prediction is possible by incorporation of fuzzy logic in Adaptive Neuro Fuzzy Inference System (ANFIS). Fuzzy rules obtained from experiments are also incorporated into ANFIS to improve its learning capability. On the other hand, Non-Linear Regression (NLR) is employed more comfortably than ANFIS without need to rules. The results of sensitivity analysis indicate that increase of cement and moisture content have the most positive and negative effect, respectively, on the UCS values. It is observed a more satisfactory prediction of UCS in favour of ANFIS with 94% and 84% correlation coefficients between predicted and target values in cement and lime cases, respectively. [ABSTRACT FROM AUTHOR]

This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (Pc, kW) was measured directly on motorised head-pulleys. The Pc was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the Pc. [ABSTRACT FROM AUTHOR]

Braced frames are frequently used as lateral load resisting systems in steel buildings due to their cost-effectiveness and efficiency in resisting earthquake loads. Evaluating the seismic loads on those buildings requires estimating the fundamental period of vibration of the buildings at first. Most design codes use empirical formulas that depend only on the height of the buildings without considering the effect of bracing configurations or building irregularities. This paper aims to develop new equations to estimate the fundamental period of vibration of irregular steel buildings occupied with different bracing systems. For this purpose, 176 prototype buildings with different bracing configurations and irregularities have been selected and modeled using ETABS finite element program. Three types of irregularity have been considered: vertical, horizontal, and combined irregularity. Then, the fundamental periods of vibration have been estimated through analysis and optimal building design using nonlinear regression analysis. According to the findings, the configuration of the bracing system and the building irregularity influence the fundamental period of vibration of buildings of the same height, where vertical and combined irregularities decrease the period values by about 20% for CBFs. This indicates the importance of incorporating new equations in calculating the fundamental period of these types of steel structures. [ABSTRACT FROM AUTHOR]

This paper attempts to investigate the impacts of inter-city high-speed rail (HSR) on tourism arrivals by employing a novel data of check-ins generated from social media. This type of check-in data collected in a case study (Hangzhou, China) reveals its high correlation with tourism activities and is feasible to act as a proxy of real tourism arrivals. A nonlinear regression model is developed to discover the temporal redistribution of tourism arrivals caused by HSR on weekends and holidays. Results show that Nanjing-Hangzhou HSR can significantly raise the number of visitors from Nanjing with a growth of 29.44% on Saturdays and 41.72% on Sundays. Further analysis on hourly distribution of these check-ins on weekends detects early arrival on Friday nights and longer stay on Sunday afternoons after HSR operates. Moreover, negative effect of seasonal climate change on tourism is also alleviated by HSR. This paper verifies the effectiveness of social media check-in data in tourism research and proposes practicable methodologies to quantitatively analyse this type of data. [ABSTRACT FROM AUTHOR]

Understanding airfield pavement deterioration is essential for airport asset management to ensure safe and efficient airport operations. This paper employs Gradient Boosting Machine (GBM) – a machine learning method – to investigate the contributions of a variety of influencing factors to runway and taxiway pavement deterioration at Chicago O'Hare International Airport. By adopting a systematic procedure consisting of model training, validation, and testing, two separate GBM models are developed to estimate Pavement Condition Index (PCI) of runways and taxiways. The models account for various input variables that are believed to affect pavement deterioration, including pavement age and material, maintenance and rehabilitation history, weather conditions, and air traffic loading effects. The developed GBM models are shown to outperform other methods (including linear regression, nonlinear regression, artificial neural networks, and random forest) in terms of model goodness-of-fit for both runway and taxiway pavements. The GBM modelling results are subsequently used to interpret the influence of individual input variables as well as their interactions on PCI, using relative importance and partial dependence plots. With promising results, the study demonstrates the use of an approach that was not previously considered in infrastructure management and can help airport agencies enhance the ability to understand airport asset performance. [ABSTRACT FROM AUTHOR]

In the paper an exponential bound for the probabilities of large deviations of the normalized residual correlogram as an estimator of a random stationary Gaussian noise covariance function in continuous time nonlinear functional regression model is obtained. The strongest known result on weak consistency of the residual correlogram is a corollary of this fact. [ABSTRACT FROM AUTHOR]

Quantile regression has great flexibility in describing the effect of covariate on response. In this paper, based on the censored median regression proposed by Zhou, we develop a new weighted quantile regression of nonlinear models when the response is randomly censored. Our method can be applied to more complicated quantile regression models at any quantile within (0, 1), which contains censored median regression. Furthermore, the consistency and asymptotic normality of the proposed estimator are derived under appropriate assumptions. Finally, the finite sample performance of the proposed estimator is examined by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper focuses on the corrections to the score test statistic under the exponential family nonlinear model. We use Monte Carlo simulations to compare the corrected statistics and their uncorrected versions and to examine the impact of the number of nuisance parameters on their finite-sample behaviors for the normal nonlinear regression model. The numerical results have shown that the corrected score statistic performs better than the uncorrected version. Finally, we perform a statistical analysis with real data by using the approach proposed in the article. [ABSTRACT FROM AUTHOR]

Modelling the pavement deterioration process is essential for a successful pavement management system (PMS). The pavement deterioration process is highly influenced by uncertainties related to data acquisition and condition assessment. This paper presents a novel approach for predicting a pavement deterioration index. The model builds on a negative binomial (NB) regression used to predict pavement deterioration as a function of the pavement age. Network-level pavement condition models were developed for interstate, primary, and secondary pavement road families and were compared with traditional non-linear regression models. The linear empirical Bayesian (LEB) approach was then used to improve the predictions by combining the deterioration estimated by the fitted model and the observed/measured condition recorded in the PMS. The proposed approach can improve the mean square error prediction of the next-year pavement condition by 33%, 36% and 41% for Interstate, Primary, and Secondary roads, respectively, compared with the measured pavement condition without further modelling of the pavement deterioration. [ABSTRACT FROM AUTHOR]

In this paper, by considering an (n + 1) -dimensional random vector ( X 1 , X 2 , .... , X n , Y) T from the multivariate generalized hyperbolic (GH) distribution, we derive the joint distribution of Y and the order statistics from ( X 1 , X 2 , .... , X n) T. We show that this joint distribution is indeed a mixture of the selection GH distributions. The results were applied to some regression functions via the order statistics and established with a real data set. [ABSTRACT FROM AUTHOR]

Monthly Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) composite data are widely used in research, such as estimations of socioeconomic parameters. However, some surface conditions affect the VIIRS DNB radiance, which may create some estimation bias in certain regions. In this paper, we propose a novel normalization algorithm for VIIRS DNB monthly composite data. The aim is to normalize VIIRS radiance, collected under different surface conditions, to a reference point, so that the bias is reduced. The algorithm is based on the utilization of stable lit pixels as a reference and a nonlinear regression algorithm, to match un-normalized data to the reference data. Experimental results show that the algorithm could improve correlation (R2) between the total sum of nightlights (TOL), electric power consumption (EPC), and gross domestic product (GDP) at both a global and local scale. The algorithm could significantly diminish the seasonal component of un-normalized nightlights radiance caused by snow. The intensified nightlights radiance in sandy regions could also be reduced to a more reasonable range in comparison with other regions. Visual inspection shows that the brightness of snow-affected and sandy regions was strongly reduced after undergoing normalization. [ABSTRACT FROM AUTHOR]

In this paper, error function was proposed for the first time to predict the normalized flux in dead-end microfiltration process at different operating conditions (transmembrane pressure, concentration, and temperature) using various feed suspensions and membranes. The results showed that the predictions of proposed model had good agreements with experimental data. In addition, model parameter k is analyzed quantificational by using multivariate nonlinear regression method. Moreover, the proposed model also showed high predictive accuracy (2.88%) than cake models (4.48%), and two empirical models (A (6.19%) and B (35.7%)). [ABSTRACT FROM AUTHOR]

The presence of defects in industrial manufacturing may compromise the final quality and cost of a product. Among all possible defect causes, human errors have significant effects on the performances of assembly systems. Much research has been conducted in recent years focusing on the problem of defect generation in assembly processes, considering the close connection between assembly complexity and human errors. It was observed that the relationship between the average number of defects introduced during each assembly phase and the related assembly complexity follows a power-law relationship. Accordingly, many authors proposed a data logarithmic transformation in order to linearize the mathematical model. However, as has already been discussed in literature, when the model is retransformed in the original form a significant bias may occur, leading to completely wrong predictions. In this paper, the bias due to the logarithmic transformation of models for predicting defects in assembly is analyzed and discussed. Two alternative methods are proposed and compared to overcome this drawback: the use of a bias correction factor to the retransformed fitted values and a power-law nonlinear regression model. The latter has proved to be the best approach to predict defects with few non-repeated data and affected by high variability, such as in the case under study. [ABSTRACT FROM AUTHOR]

Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSSmin) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from 2011 Youth Risk Behavior Surveillance System (YRBSS) data are used to illustrate the procedures developed in this paper. [ABSTRACT FROM AUTHOR]

The heteroscedastic nonlinear regression model (HNLM) is an important tool in data modeling. In this paper we propose a HNLM considering skew scale mixtures of normal (SSMN) distributions, which allows fitting asymmetric and heavy-tailed data simultaneously. Maximum likelihood (ML) estimation is performed via the expectation-maximization (EM) algorithm. The observed information matrix is derived analytically to account for standard errors. In addition, diagnostic analysis is developed using case-deletion measures and the local influence approach. A simulation study is developed to verify the empirical distribution of the likelihood ratio statistic, the power of the homogeneity of variances test and a study for misspecification of the structure function. The method proposed is also illustrated by analyzing a real dataset. [ABSTRACT FROM AUTHOR]

Some recent work on the analysis of nonlinear and nonstationary time series models is reviewed. A couple of novel results are obtained in extending nonlinear cointegrating regression models to a time series situation. All through the paper focus is on aspects that could lead to a more well-defined concept of nonlinear cointegration. [ABSTRACT FROM AUTHOR]

Extreme-value theory is useful for modelling extremal events. The behaviour of such extremal events may be impacted by other variables and such dependence is captured using a regression framework. In this paper, we develop residual based diagnostic analysis, generalized leverage, generalized Cook's distance and also global and local influence analysis for the generalized extreme-value nonlinear regression model. Two residuals for use with the model are proposed: the standardized and deviance residuals. Additionally, we present a model misspecification test that can be used to determine whether the fitted model is incorrectly specified. We also show how to perform nonnested testing inferences. Empirical applications based on simulated and observed data are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we report the optimization and development of predictive models for the corrosion inhibition of mild steel in sulphuric acid by Methyl-5-benzoyl-2-benzimidazole Carbamate (Mebendazole). Design expert was used to analyze the corrosion inhibition related process parameters, such as corrosion penetration rate, inhibition efficiency, inhibitor concentration, acid concentration, weight loss, and their relationships. An attempt is made to obtain the optimal settings for this corrosion inhibition related process parameters. Design methodology, weight loss measurement, open-circuit potential analysis, Tafel polarization, etc. were used for the evaluation of inhibition efficiency of mebendazole for mild steel in H2SO4. The corrosion inhibition process parameters were optimized and predictive mathematical models developed using the Response Surface Methodology (RSM) using the central composite design (CCD) tool of Design Expert software version 11. Experimental and theoretical corrosion inhibition parameters were used to develop a nonlinear regression model to predict the optimal inhibition efficiency. Also, a quadratic model was generated, with predicted optimum inhibition efficiency of 88.4095% obtained having very near unity desirability of 0.914. The developed model shows that inhibition efficiency is related to the inhibitor concentration, immersion time, and acid concentration. The regression models generated are successfully used to predict the corrosion inhibition behaviour of mebendazole for low carbon steel in sulphuric acid medium. [ABSTRACT FROM AUTHOR]

Regression is a common technique in engineering when physical laws are unknown. Practitioners usually look for a unique set of true parameters that optimally explain the observed data. This is, for instance, the case in concrete strength estimation where engineers have been looking for an universal law to estimate this magnitude. We show that this approach is incorrect if the uncertainty of the regression problem is not properly taken into account. The uncertainty analysis of linear regression problems is revisited providing an analytical expression for the direction of maximum uncertainty where most of the models are sampled when partial information is used. We also analyse the case of 1D nonlinear regression models (exponential and potential models) and the multivariate case. We show a simple way of sampling the posterior distribution of the model parameters by performing least-squares of different data bags (bootstrap), introducing the percentile curves for the concrete strength estimation, comparing the results obtained from the linearized and nonlinear bootstrap procedures in the case of the nonlinear regression models. The methodology introduced in this paper constitutes a robust and simple way of assessing the intrincic uncertainty of these well-known parameter identification problems and adopting more robust decisions. [ABSTRACT FROM AUTHOR]

This paper develops a testing procedure to simultaneously check (i) the independence between the error and the regressor(s), and (ii) the parametric specification in nonlinear regression models. This procedure generalizes the existing work of Sen and Sen ["Testing Independence and Goodness-of-fit in Linear Models," Biometrika, 101, 927–942.] to a regression setting that allows any smooth parametric form of the regression function. We establish asymptotic theory for the test procedure under both conditional homoscedastic error and heteroscedastic error. The derived tests are easily implementable, asymptotically normal, and consistent against a large class of fixed alternatives. Besides, the local power performance is investigated. To calibrate the finite sample distribution of the test statistics, a smooth bootstrap procedure is proposed and found work well in simulation studies. Finally, two real data examples are analyzed to illustrate the practical merit of our proposed tests. [ABSTRACT FROM AUTHOR]

In this paper, we examine a nonlinear regression (NLR) model with homoscedastic errors which follows a flexible class of two-piece distributions based on the scale mixtures of normal (TP-SMN) family. The objective of using this family is to develop a robust NLR model. The TP-SMN is a rich class of distributions that covers symmetric/asymmetric and lightly/heavy-tailed distributions and is an alternative family to the well-known scale mixtures of skew-normal (SMSN) family studied by Branco and Dey [35]. A key feature of this study is using a new suitable hierarchical representation of the family to obtain maximum-likelihood estimates of model parameters via an EM-type algorithm. The performances of the proposed robust model are demonstrated using simulated and some natural real datasets and also compared to other well-known NLR models. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model is developed by integrating a non-linear regression model and optimization-driven deep learner for video super resolution. Initially, the low-resolution frames are subjected to framing, and each frame is provided to both Fractional-Group Search Optimizer-based Deep Belief Network (FrGSO-DBN) classifier and the nonlinear regression model. Then, the output frames of both methods are averaged by the proposed hybrid model and the enhanced video frame is generated. Here, the FrGSO is developed by modifying the update process of the GSO using the fractional theory, to train the DBN such that the weights in the DBN are selected optimally. Finally, the simulation results reveal that the proposed hybrid model achieves high values of 0.8697, 25.637 dB, 0.9171, and 49.821 dB, for the metrics Structural Similarity Index Measure, Peak Signal to Noise Ratio, Feature Similarity Index Measure, and Second Derivative like Measurement of Enhancement, respectively. [ABSTRACT FROM AUTHOR]

This paper considers nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with both multiplicative and additive distortion measurement errors. We propose conditional variance and conditional mean calibration estimation methods for the unobserved variables, then a nonlinear least squares estimator is proposed. For the hypothesis testing of parameter, a restricted estimator under the null hypothesis and a test statistic are proposed. The asymptotic properties for the estimator and test statistic are established. Lastly, a residual-based empirical process test statistic marked by proper functions of the regressors is proposed for the model checking problem. We further suggest a bootstrap procedure to calculate critical values. Simulation studies demonstrate the performance of the proposed procedure and a real example is analysed to illustrate its practical usage. [ABSTRACT FROM AUTHOR]

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data. [ABSTRACT FROM PUBLISHER]

In this paper we investigate the problem of designing experiments for generalized least-squares analysis in the Michaelis–Menten model. We study the structure of exactD-optimal designs in a model with an autoregressive error structure. Explicit results for locallyD-optimal designs are derived for the case where two observations can be taken per subject. Additionally standardized maximinD-optimal designs are obtained in this case. The results illustrate the enormous difficulties to find exact optimal designs explicitly for nonlinear regression models with correlated observations. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to develop diagnostics analysis for nonlinear regression models (NLMs) under scale mixtures of skew-normal (SMSN) distributions introduced by Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124]. This novel class of models provides a useful generalization of the symmetrical NLM [Vanegas LH, Cysneiros FJA. Assessment of diagnostic procedures in symmetrical nonlinear regression models. Comput. Statist. Data Anal. 2010;54:1002–1016] since the random terms distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as the skew-t, skew-slash, skew-contaminated normal distributions, among others. Motivated by the results given in Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124], we presented a score test for testing the homogeneity of the scale parameter and its properties are investigated through Monte Carlo simulations studies. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. The newly developed procedures are illustrated considering a real data set. [ABSTRACT FROM PUBLISHER]