Normal Error Distribution Research Articles

Modeling and Forecasting the Volatility of Returns in the Infrastructure Sector in Emerging Markets

Understanding the volatility behaviour of specific sectors of the economy enables investors to formulate workable investment strategies, and policy-makers to formulate policies that dampen excess volatility. This study examined the volatility features of the infrastructure sector in emerging markets. The features assessed were the GARCH effects, volatility persistence, and leverage effects. EGARCH and GJRGARCH models of order one under normal and non-normal error distributions were employed to unpack the volatility behaviour of infrastructure returns in emerging markets. The results from both models under all distributions indicated the existence of GARCH effects, volatility clustering, volatility persistence, and leverage effects in the infrastructure sector in emerging nations. This implies that past conditional variance is significant in determining current conditional variance, thereby rendering forecasting a worthwhile task. The findings also suggest that investors interested in the infrastructure sector in emerging markets should incorporate leverage effects in their estimation of value-at-risk. Furthermore, they should focus on factors other than mean-variance portfolio optimization and consider leverage effects, excess kurtosis, and skewness when making investment decisions. Finally, investors in the infrastructure sector in emerging markets are encouraged to formulate hedging strategies as they are exposed to significant risk and uncertainty.

Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.

Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin–Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.

The partial banana mapping: a robust linear method for impact probability estimation

ABSTRACT This paper presents a robust linear method for impact probability estimation of near-Earth asteroids with the Earth. This method is a significantly modified and improved method, which uses a special curvilinear coordinate system associated with the nominal orbit of an asteroid. One of the coordinates of this system is the mean anomaly in the osculating orbit of an asteroid. A normal distribution of errors of coordinates and velocities of this system is assumed. Because of the usage of the curvilinear coordinate system, the fact that the confidence region is curved and stretched mainly along the nominal asteroid orbit is taken into account. On the main axis of the curvilinear confidence ellipsoid the virtual asteroid, which is the closest to the Earth, is found. The part of the curvilinear confidence ellipsoid, around the found virtual asteroid, is obtained and mapped on to its target plane. The impact probability is calculated as the probability of the asteroid being in the region of the found virtual asteroid multiplied by the probability of a collision of the found virtual asteroid with the Earth. This approach is shown to give more accurate and trustworthy results than the target plane method.

Optimal traffic control at smart intersections: Automated network fundamental diagram

Recent advances in artificial intelligence and wireless communication technologies have created great potential to reduce congestion in urban networks. In this research, we develop a stochastic analytical model for optimal control of communicant autonomous vehicles (CAVs) at smart intersections. We present the automated network fundamental diagram (ANFD) as a macro-level modeling tool for urban networks with smart intersections. In the proposed cooperative control strategy, we make use of the headway between the CAV platoons in each direction for consecutive passage of the platoons in the crossing direction through non-signalized intersections with no delay. For this to happen, the arrival and departure of platoons in crossing directions need to be synchronized. To improve system robustness (synchronization success probability), we allow a marginal gap between arrival and departure of the consecutive platoons in crossing directions to make up for operational error in the synchronization process. We then develop a stochastic traffic model for the smart intersections. Our results show that the effects of increasing the platoon size and the marginal gap length on the network capacity are not always positive. In fact, the capacity can be maximized by optimizing these cooperative control variables. We analytically solve the traffic optimization problem for the platoon size and marginal gap length and derive a closed-form solution for a normal distribution of the operational error. The performance of the network with smart intersections is presented by a stochastic ANFD, derived analytically and verified numerically using the results of a simulation model. The simulation results show that optimizing the control variables increases the capacity by 138% when the error standard deviation is 0.1 s.

Four Assumptions of Multiple Regression That Researchers Should Always Test.

Most statistical tests rely upon certain assumptions about the variables used in the When these assumptions are not met the results may not be trustworthy, resulting in a Type I or Type II error, or overor under-estimation of significance or effect size(s). As Pedhazur (1997, p. 33) notes, Knowledge and understanding of the situations when violations of assumptions lead to serious biases, and when they are of little consequence, are essential to meaningful data analysis. However, as Osborne, Christensen, and Gunter (2001) observe, few articles report having tested assumptions of the statistical tests they rely on for drawing their conclusions. This creates a situation where we have a rich literature in education and social science, but we are forced to call into question the validity of many of these results, conclusions, and assertions, as we have no idea whether the assumptions of the statistical tests were met. Our goal for this paper is to present a discussion of the assumptions of multiple regression tailored toward the practicing researcher. Several assumptions of multiple regression are “robust” to violation (e.g., normal distribution of errors), and others are fulfilled in the proper design of a study (e.g., independence of observations). Therefore, we will focus on the assumptions of multiple regression that are not robust to violation, and that researchers can deal with if violated. Specifically, we will discuss the assumptions of linearity, reliability of measurement, homoscedasticity, and normality.

A new method for automated estimation of joint roughness coefficient for 2D surface profiles using power spectral density

In this study, a new method for the objective and accurate estimation of the joint roughness coefficient (JRC) of surface profiles, which are extracted from terrestrial laser scanner point clouds, is proposed. The requirements of objectivity, accuracy, reliability, and suitability for automatic analysis have been the basic criteria in assessing the performance of the procedure for JRC estimation. The procedure to estimate the JRC value of a sample profile is based on a similarity measure, between the third-order polynomial function fits to the power spectral density in the spatial frequency domain of the sample surface profile and Barton's reference profiles. The procedure is tested on the one hundred and two digitized surface profiles found in the literature. Normal probability density distribution of estimation errors of the results show that the JRC estimation by the proposed method is more accurate and precise compared to the results from the three versions of the well-known and commonly used Z2 method.

A nonparametric procedure for changepoint detection in linear regression

Changepoint detection in linear regression has many applications in climatology, bioinformatics, finance, oceanography and medical imaging. In this article, we propose a procedure to detect changepoint in linear regression based on a nonparametric method. The proposed procedure performs well for non normal error distribution and does not require the assumption of normal distribution. A simulation study is conducted to compare the performance of the proposed procedure with the existing procedure, considering the error distribution as Laplace, Student’s t, and mixture of normal distributions. The simulation study indicates that the proposed procedure outperforms its competitor. A real-life example is used to illustrate the working procedure.

Dependence Modeling and Portfolio Risk Estimation using GARCH-Copula Approach

Past studies have shown that linear correlation measure may result in misleading interpretations and implications of dependency when financial variables are involved. The copula approach can be adopted as an alternative for measuring dependence as it provides the solution to fat tail problems in multivariate cases which arises from the probability of large or extreme co-movements. Due to limited studies on copulas using Islamic financial data, this study set outs to obtain a clear picture on the dependence between Islamic and conventional stock markets in Malaysia. Firstly, we model the dependence between Islamic and conventional returns data using the copula-ARMA-GARCH models with normal and non-normal error distributions, and secondly, we evaluate the portfolios of Islamic and conventional indices using recent risk measures. This paper shows that, by using the copula approach for measuring the dependency between two financial variables while maintaining their true nature as described by the ARMA-GARCH models, meaningful interpretation can be made about the association of the financial variables which reflects the real association between markets. Furthermore, this study proposes a set of procedures on how portfolio risks can be estimated using VaR based on the ARMA(p,q)-GARCH(1,1)-t-copula models including backtesting via simulation.

CENTRAL BLOOD PRESSURE

Objective: To compare the specific waveforms obtained invasively in the aorta with the corresponding waveforms obtained from a Mobil-O-Graph device and to compare the calculated central blood pressures with the aim to validate the noninvasive central blood pressure measurement. Design and method: Twenty subjects (10 males, 68 ± 12y, BMI: 27.4 ± 4.6Kg/m2 and 10 females, 77 ± 8y, BMI: 28.5 ± 5.3Kg/m2), submitted for invasive coronary evaluations, were included in the study. In the same moment, in which the hemodynamic study was performed, the Mobil O Graph acquisition data was performed. Once a single beat was obtained (invasive and non-invasive central pressure), both blood pressure waves were digitized and stored for subsequent analysis and calculations. A computerized interpolation procedure was developed in our laboratory to compare invasive and non-invasive central blood pressure waves. All waveforms were stable and no variability was detected in their periods. Usual medications were not withheld, but no vasoactive drug was administered during the measurement. Statistical Analysis: All data are reported as mean ± standard deviation. The analysis of invasive and non-invasive central pressure was evaluated using unpaired Student t-test and a Bland & Altman approach. A p < 0.05 was considered as a significant difference (SPSS software). Results: A high correlation between Mobil O Graph cBP compared to the hemodynamic values was found in men (r < 0.89) and women ((r < 0.917). Moreover, mean differences (hemodynamic minus Mobil o Graph) were in the 95% confidence interval and showed a normal error distribution in men and women. However, in both genders, the slope of the regression lines (Men: y = 0.7673x + 16.301; Women: y = 0.7874x + 22.008) was lower than 1, indicating a lower assessment of Mobil O Graph cBP values respective to the hemodynamic ones. Conclusions: The main result was the high correlation, either in men or women, between cBP measured with the Mobil O Graph device or invasively and the underestimation of CBP values measured by the Mobil O Graph device.

RELATION TO FINANCIAL DISTRESS THROUGH TRANSACTIONS WITH AFFILIATED ENTITIES

The purpose of this study was to investigate the relationship between financial distress and transactions with affiliated individuals. In this research financial information of 152 companies accepted in Tehran Stock Exchange during the period of 1391 to 1396 has been investigated. The purpose of this study is applied. In terms of collecting and analyzing data, this research is a descriptive correlation method. From the point of view of information gathering, post-event research is used to analyze the data using descriptive, inferential statistics and multi-variable regression analysis. . &#x0D; This research has been investigated with two hypotheses by ERW software. In this regard, in the initial implementation of the first model, using the printing statistics and Yasmin, the appropriate regression model model (computational or stationary data with random effects) has been determined and using statistics such as ; The novelty of this and Chou's reliability of variables are investigated. Then, in the second run, the classic regression assumptions model, including the normal distribution of variables, the independence of the distribution of errors, the normal distribution of errors, the heterogeneity of variances and the linear relationship between independent and control variables, have been investigated. Finally, the results of the research show that financial distress through transactions with affiliated persons but the size of the auditor is not relevant to transactions with affiliated persons.

Generalized signed-rank estimation for regression models with non-ignorable missing responses

In regression modeling, it has become very common to deal with missing data, which render the statistical analysis more difficult. It is then of interest to develop robust methods toward statistical inference for the regression coefficients in the presence of missing data. From this, a generalized signed-rank estimator of the regression coefficients in a model with non-ignorable missing responses is studied. The generalized signed-rank objective function covers a large class of existing objective functions such as the least squares, the signed-rank, the least absolute deviation among others. Under mild regularity conditions, the consistency and the limiting distribution of the proposed estimator are established. Finite-samples simulation studies are carried out to assess the performance of the proposed estimation method, and a practical real data application is given to illustrate our method. Results of these studies show that the proposed approach results in a robust and more efficient estimator compared to the least squares and least absolute deviation approaches, when dealing with heavy-tailed, contaminated model error distributions and/or when data contain gross outliers in the response space. Under the standard normal model error distribution, while the least squares and the proposed estimator have a similar performance, the least absolute deviation is inefficient.

The new formation bulk density predictions for siliciclastic rocks

Accurate determination of the overburden pressure obtained by integrating the formation bulk density from surface to the depth of interest is very critical to pore pressure prediction. When information about the formation bulk density is not available, the current practice is to estimate formation bulk density from compressional-wave velocity using empirical relationships. There is no single formation bulk density prediction model that considers lithologic variation in siliciclastic settings. This imposes severe limitations on the application of the existing empirical relationships to any lithological column that consists of several stratigraphic units. In this paper, attempt is made to develop the new formation bulk density prediction models that can be applied to a wide range of lithologies in siliciclastic environments. The new models are validated using wireline log data acquired from two wells in the tertiary deltaic system of the Niger Delta basin. In the new models, formation bulk density is expressed as a function of compressional-wave velocity and shale volume factor. The accuracy of the new models is quantified using statistical analysis. In general, a good agreement exists between the predicted and measured formation bulk density using the newly proposed models. When compared to the existing models, the new models clearly outperform the most widely used empirical relationships. The new models produce the least root-mean-square errors, normal error distributions and least residual values. Unlike any of the existing empirical relationships, the newly developed models can be applied to clean sands, shaly sands and shales with good accuracy.

Use of infrared matrix sensor for temperature measurement and monitoring of PEM/FC stacks

Cells composing FC stacks can be subject to distinct operating conditions, resulting in different temperatures between them. This procedure establishes a non-homogeneous distribution along the stack profile. For a more precise monitoring and control purposes, the location of the cell’s temperature measurements has to be right on the surface of the stack profile. Thus, the greater the number of temperature measurement points on the stack profile, the greater is its control reliability and safer the operating conditions. As the fuel cell composition is almost entirely made of graphite and constantly subjected to an electrical potential difference, the direct contact of a temperature sensor with the stack must be avoided. The purpose of this paper is to propose a method to monitor the FC stack temperature distribution by an infrared matrix sensor. This procedure will obtain an increased number of measuring points avoiding any physical contact with the cells. This paper deals with information obtained from an infrared matrix sensor with its calibration handled by software. After processing the sensor's temperature data, they are displayed as a thermal image. With this, the temperature control of the stack can act in such a way that it does not reach critical levels that could compromise its useful life. In this text, a sensor calibration procedure is presented together with data validation through experimental results. In the tests using this technique was verified that about 84% of the normal error distribution around the maximum temperature was kept between ± 2 °C, therefore within tolerable limits.

GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market?

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The aim of this article is to compare the GARCH (Generalised Auto Regressive Conditional Heteroskedasticity) family models —GARCH (1.1), GJR-GARCH, PGARCH, EGARCH, and IGARCH— with the EWMA (Exponentially Weighed Moving Average) model in the hope of finding the best model to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01/04/1993 and 30/08/2016. We find that the asymmetric model IGARCH following a normal error distribution yields the best forecasting performance results and therefore, surpasses the EWMA model. Our results could have application in the risk management in Morocco, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack of previous similar studies.

Parameter Estimation and Uncertainty Analysis of ORYZA_V3 Model Using the GLUE Method

Abstract. The Generalized Likelihood Uncertainty Estimation (GLUE) method is one of the popular methods for parameter estimation and uncertainty analysis, although it has been criticized for some drawbacks in numerous studies. In this study, we performed an uncertainty analysis for the ORYZA_V3 model using the GLUE method integrated with Latin hypercube sampling (LHS). Different likelihood measures were examined to understand the differences in derived posterior parameter distributions and uncertainty estimates of the model predictions based on a variety of observations from field experiments. The results indicated that the parameter posterior distributions and 95% confidence intervals (95CI) of model outputs were very sensitive to the choice of likelihood measure, as well as the weights assigned to observations at different dates and to different observation types within a likelihood measure. Performance of the likelihood measure with a proper likelihood function based on normal distribution of model errors and the combining method based on mathematical multiplication was the best, with respect to the effectiveness of reducing the uncertainties of parameter values and model predictions. Moreover, only the means and standard deviations of observation replicates were enough to construct an effective likelihood function in the GLUE method. This study highlighted the importance of using appropriate likelihood measures integrated with multiple observation types in the GLUE method. Keywords: GLUE, Likelihood measures, Model uncertainty, Crop model.

Optimizing Service Restoration in Distribution Systems With Uncertain Repair Time and Demand

This paper proposes a novel method to co-optimize distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the Progressive Hedging algorithm, is developed for solving large-scale outage management problems in an effective and timely manner. The proposed method is validated on modified IEEE 34- and 8500-bus distribution test systems.

Covariates missing at random under signed-rank inference

A robust regression analysis in the presence of missing covariates is considered. The signed-rank estimator of the regression coefficients is studied, where the missing covariates are imputed under the assumption that they are missing at random. The consistency and asymptotic normality of the proposed estimator are established under mild conditions. Monte Carlo simulation experiments are carried out. They demonstrate that the signed-rank estimator is more efficient than the least squares and the least absolute deviations estimators whenever the error distribution is heavy tailed or contaminated. Under the standard normal model error distribution with well specified conditional distribution of the missing covariates, the least-squares and signed-rank methods provide similar results while the least absolute deviations method is inefficient. Finally, the use of the proposed methodology is illustrated using the economic and political data on nine developing countries in Asia from 1980 to 1999.

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

ABSTRACTAs sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.

A new permutation test statistic forK‐sample multivariate designs

SummaryWe consider permutation tests based on a likelihood ratio like statistic for the one way ork‐sample design where the observations arel‐dimensional vectors. This generalizes both the univariatek‐sample case and the two sample multivariate case used in the examples of Kolassa and Robinson. We also give a saddlepoint approximation for the permutation test. Numerical examples are given to illustrate the accuracy of the saddlepoint approximation and the improvement in the power of the tests compared to the classical statistics in the case of long tailed error distributions and no loss of power for normal error distributions.

Dose–volume toxicity modeling for de-intensified chemo-radiation therapy for HPV-positive oropharynx cancer

Background and purposeThe aim is to determine the radiobiological parameters of four popular normal tissue complication probability (NTCP) models that describe the dose–response relations of salivary glands and pharyngeal constrictors to the severity of patient reported xerostomia and dysphagia, respectively 6 and 12months post chemo-radiotherapy, furthermore, to evaluate the goodness-of-fit of the NTCP models for different combinations of glands and constrictors. Material and methodsForty-three patients were treated on a prospective multi-institutional phase II study (ClinicalTrials.gov, NCT01530997) assessing the efficacy of de-intensified chemoradiotherapy in patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma. All patients received 60Gy intensity modulated radiotherapy with concurrent weekly intravenous cisplatinum. All patients reported severity of their xerostomia and dysphagia (pre- and post-treatment) using the patient reported outcome version of the CTCAE (PRO-CTCAE). A change in severity (from baseline) of ≥2 was considered clinically meaningful. The Lyman–Kutcher–Burman (LKB), Relative Seriality (RS), Logit, and Relative Seriality Logit (RSL) NTCP models were used to fit the patients’ dose/volume data to changes in PRO-CTCAE severity of xerostomia and dysphagia (from baseline to 6 and 12months post-treatment). The correlation of the models with the patient outcomes was performed for different combinations of salivary glands and different sections of pharyngeal constrictors. The goodness-of-fit of the different models was assessed through the area under the receiver operating characteristic curve (AUC), maximum of the log-likelihood function, normal error distribution and Akaike information criterion (AIC). ResultsThe dose/volume metrics of the combined contralateral (parotid+submandibular) glands appear to correlate best with xerostomia, at both 6- and 12-months. Among the different sections of pharyngeal constrictors, the dose/volume metrics of the superior pharyngeal constrictors appear to correlate best with dysphagia at 6months. The AUC values ranged from 0.72 to 0.85 in the case of xerostomia and 0.73 to 0.74 in the case of dysphagia over the different models. The four NTCP models showed similar goodness-of-fit. The differences in AIC between the different models were less than 2 and ranged within 0.7 and 0.8 in the cases of xerostomia and dysphagia, respectively. The calculated parameters of the LKB model were D50=26.9Gy, m=0.63, n=1.0 for the combined contralateral glands at 12months and D50=62.0Gy, m=0.10, n=0.49 for the superior pharyngeal constrictors at 6months. ConclusionsThe values of the parameters of four NTCP models were determined for salivary glands and pharyngeal constrictors. All four models could fit the clinical data equally well. The NTCP predictions of the combined contralateral glands and superior pharyngeal constrictors showed the best correlation with the patient reported outcomes of xerostomia and dysphagia, respectively.