Forecasting country conflict using statistical learning methods

High viscosity of quench oil is a critical problem of quench system in ethylene cracking furnaces in petrochemical plant, due to its influences on the safety and stability of equipments, meanwhile, the variety of viscosity of quench oil has a negative impact on yield of ethylene and other chemical products. This paper presents a new statistical learning model to forecast the real-time variety of quench oil viscosity based on statistical algorithm and machine learning method. Firstly, statistical algorithm is applied to reduce dimension of parameters, secondly, fitting real-time predictive model through machine learning method. The simulation results shows that this model can monitor the variety of viscosity per hour according to identified controllable parameters which are highly correlated with viscosity of quench oil.

The use of statistical learning methods has recently increased within the risk assessment literature. They have primarily been used to increase accuracy and the area under the curve (AUC, i.e., discrimination). Processing approaches applied to statistical learning methods have also emerged to increase cross-cultural fairness. However, these approaches are rarely trialed in the forensic psychology discipline nor have they been trialed as an approach to increase fairness in Australia. The study included 380 Aboriginal and Torres Strait Islander and non-Aboriginal and Torres Strait Islander males assessed with the Level of Service/Risk Needs Responsivity (LS/RNR). Discrimination was assessed through the AUC, and fairness was assessed through the cross area under the curve (xAUC), error rate balance, calibration, predictive parity, and statistical parity. Logistic regression, penalized logistic regression, random forest, stochastic gradient boosting, and support vector machine algorithms using the LS/RNR risk factors were used to compare performance against the LS/RNR total risk score. The algorithms were then subjected to pre- and postprocessing approaches to see if fairness could be improved. Statistical learning methods were found to produce comparable or marginally improved AUC values. Processing approaches increased several fairness definitions (namely xAUC, error rate balance, and statistical parity) between Aboriginal and Torres Strait Islanders and non-Aboriginal and Torres Strait Islanders. The findings demonstrate that statistical learning methods may be a useful approach to increasing the discrimination and cross-cultural fairness of risk assessment instruments. However, both fairness and the use of statistical learning methods encompass significant trade-offs that need to be considered. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Setting up workable budgets symbolizes the competence of state highway agencies (SHAs) in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary estimates. Hence, based on the 1,249 projects retrieved from the Florida Department of Transportation (FDOT) database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions. However, the currently used methods have serious limitations. This study introduced alternative statistical learning approaches to the currently most used methods: least absolute shrinkage and selection operator (LASSO) and general regression neural network (GRNN). LASSO regression, for instance, has proved in other areas of science to be remarkably better in terms of variable selection, interpretability, and numerical stability. In addition, this study also accounted for economic factors in model development because economic conditions are influential on highway construction costs but have received limited attention. Using the same dataset, LASSO and GRNN models were developed, and then their performances were evaluated based on a set of criteria, e.g., the mean absolute error and mean absolute percentage error. In comparison to the current practice with state DOTs, this research contributes to the body of knowledge by introducing a series of objective modeling approaches that can prevent human errors, requiring no substantial experience in preliminary estimating. Besides the introduction of statistical learning methods, this study took economic indicators into account when developing the models because they are important factors but have been ignored in previous studies. In addition, these statistical learning methods can produce reliable estimates in a much faster and more consistent fashion, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate.

Extreme warm water events, known as marine heatwaves, cause a variety of adverse impacts on the marine ecosystem. They are occurring more and more frequently across the global ocean. Yet monitoring marine heatwaves below the sea surface is still challenging due to the sparsity of in situ temperature observations. Here, we propose a statistical learning method guided by ocean dynamics and optimal prediction theory, to detect subsurface marine heatwaves based on the observable sea surface temperature and sea surface height. This dynamics-guided statistical learning method shows good skills in detecting subsurface marine heatwaves in the oceanic epipelagic zone over many parts of the global ocean. It outperforms both the classical ordinary least square regression and popular deep learning methods that do not effectively exploit ocean dynamics, with clear dynamical interpretation for its outperformance. Our study provides a useful statistical learning method for near real-time monitoring of subsurface marine heatwaves at a global scale and highlights the importance of exploiting ocean dynamics for enhancing the efficiency and interpretability of statistical learning.

To solve the spam problem, many statistical learning methods and AIS methods have been proposed and applied. In essence, statistical learning methods and AIS methods have quite different origins, and they try to find the solutions from distinct aspects. In recent works, we proposed several hybrid methods, which combined immune theory with statistical methods in spam filtering. In this paper, we briefly review and analyze these works and possible extensions, and demonstrate the rationality of building hybrid immune models for spam filtering. In addition, a generic framework of an immune based model is presented, and online implementation strategies are given. It is well demonstrated that how to apply the immune based model to building an intelligent email server.

There is growing momentum to develop statistical learning (SL) methods as an alternative to conventional genome-wide association studies (GWAS). Methods such as random forests (RF) and gradient boosting machine (GBM) result in variable importance measures that indicate how well each single-nucleotide polymorphism (SNP) predicts the phenotype. For RF, it has been shown that variable importance measures are systematically affected by minor allele frequency (MAF) and linkage disequilibrium (LD). To establish RF and GBM as viable alternatives for analyzing genome-wide data, it is necessary to address this potential bias and show that SL methods do not significantly under-perform conventional GWAS methods. Both LD and MAF have a significant impact on the variable importance measures commonly used in RF and GBM. Dividing SNPs into overlapping subsets with approximate linkage equilibrium and applying SL methods to each subset successfully reduces the impact of LD. A welcome side effect of this approach is a dramatic reduction in parallel computing time, increasing the feasibility of applying SL methods to large datasets. The created subsets also facilitate a potential correction for the effect of MAF using pseudocovariates. Simulations using simulated SNPs embedded in empirical data-assessing varying effect sizes, minor allele frequencies and LD patterns-suggest that the sensitivity to detect effects is often improved by subsetting and does not significantly under-perform the Armitage trend test, even under ideal conditions for the trend test. Code for the LD subsetting algorithm and pseudocovariate correction is available at http://www.nd.edu/~glubke/code.html.

Water supply pipes age, deteriorate and break, which puts at risk the continuous provision of safe potable water endangering the public health in cities. Risk management methods are increasingly applied to optimise the capital investment for pipe replacement and rehabilitation, taking into account the probability and hydraulic impact of pipe breaks. As part of this process, however, historic pipe break data and statistical methods should be utilised to gather causal insights for past breaks to inform operational changes and/or capital investment decisions in order to reduce future breaks. This paper presents a comparative study of statistical and machine learning methods to carry out an exploratory causal analysis for historic pipe breaks in an operational water supply network. Regression models for count data and probabilistic models have been developed. The performance of these models was assessed and enhanced with the introduction of interactions and the inclusion of different network representations.

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

Three principal factors that influence shale gas productivity are the reservoir quality (geology), the fluid type (which affects drive mechanism), and the completion methodology (which is affected by geomechanics). Out of all the reservoir fluids, dry gas has the highest recovery factor. Within dry gas shale resources, fluid type is not as big a factor compared to other hydrocarbon types. With fluid type out of the equation, where does geology and geomechanics contribute in the assessment of type well (TW) areas? In most occurrences, the geology and geomechanics is unaccounted for and only the factors that influence or quantify productivity (EUR, Cumulative Production, etc.) are utilized to generate TW areas. This paper demonstrates a unique, innovative approach, combining well-known statistical learning methods with detailed geologic and geomechanical maps to derive "like" geologic areas, lending to common well productivity. This new approach, not only reinforces the necessity of geologic mapping work, but more importantly takes out the geologic bias, while statistically exploring the data for geologic similarities. The approach outlined in this paper takes a slightly different approach from a previous study [1], which grouped wells into different type wells by productivity measures (e.g. flowing pressures, shut in pressures, condensate gas ratio, flow potential, and separator oil and gas gravity). This approach focuses on generating similar geologic areas built around grid-based geologic and geomechanical maps, and relating these common areas back to type well performance. The common approach taken by both studies is the application of statistical learning methods, specifically principal component analysis and hierarchical clustering, to explore and group observations into similar regions. This study includes various datasets, which range from production data to detailed geologic data for a frontier gas shale play in North America, but is applicable to most unconventional gas shales. This approach utilizes unsupervised statistical learning methods and infers that we are not trying to be predictive, but rather explore what geologically is influencing the type well areas. The results of this study show that unsupervised learning techniques applied in a meaningful way can be used to delineate type well areas. Actual production data (Gas Rate and EUR) was utilized to verify the type well areas that have been generated using this method. In conclusion, six type well areas were generated and ranked by production results. It is recommended by the authors to utilize this approach in any frontier shale gas basin, in order to better streamline a type well workflow.

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression (MRL), k-dependence Bayesian (k-DB), and Random Forest (RF). The Bayesian inference and classification methods allow an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts (ADRs), performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing.

A statistical approximation learning (SAL) method is proposed for a new type of neural network: simultaneous recurrent networks (SRNs). SRNs have the ability to approximate non-smooth functions which cannot be approximated by using conventional multi-layer perceptrons (MLPs). However, most of the learning methods for SRNs are computationally expensive due to their inherent recursive calculations. To solve this problem, a novel approximation learning method is proposed by using a statistical relation between the time series of the network outputs and the network configuration parameters. Simulation results show that the proposed method can learn a strongly nonlinear function efficiently.

Traditional analytical methods use aggregate metrics, which fail to show the fine-grained patterns that emerge from trial-level fluctuations of cognitive performance due to dynamic internal states like attention, fatigue, and learning processes. This research examines how statistical and machine learning (ML) methods can analyse trial-level behavioural and physiological data to improve understanding of cognitive dynamics. A total of 9,276 trials were obtained from 112 participants who completed Stroop, N-back, and Go/No-Go tasks. The annotation of each trial included reaction time measurements alongside accuracy data, task condition information, and EEG-derived alpha power measurements. Our analysis incorporated Bayesian hierarchical models, generalized linear mixed models, state-space models, Random Forest, XGBoost, deep neural networks, and Long Short-Term Memory (LSTM) networks to forecast both reaction times and task accuracy. The LSTM model demonstrated the best predictive power by achieving R² = 0.862 for RT prediction and AUC-ROC = 0.925 for accuracy classification. The AUC-ROC score reached 0.925 for classification, while R² reached 0.862 for reaction time prediction, which proved superior to all other techniques. The predictive features of trial number, task congruency, and EEG alpha power emerged through Shapley Additive Explanations (SHAP) and LSTM saliency maps. The research demonstrates how combining statistical transparency with ML flexibility helps reveal personalized and time-dependent cognitive patterns. The proposed method provides a powerful structure for modelling trials while creating potential applications for individualized cognitive assessment systems in educational and mental health settings.

This paper is focused on evaluating the applicability of statistical machine learning methods to identifying flows and user behavior patterns at the source (client) network edge. The research was conducted in a mid-size (covering ca 150 geographically scattered locations) network developed for the Malopolska Educational Cloud (MEC) project. Due to the lack of validation sets we focused on unsupervised learning methods. Modules implementing the methods were fed with the headers of the user-generated packets; payloads were not analyzed due to privacy concerns. The presented research proved that in client edge networks even the simple classification methods yield satisfactory results in flows classification.

ABSTRACTInformation borrowing has been a general tool for improving estimation, testing, and prediction while learning from data distributed over several siloes (devices, trial groups, architecture, or sites), with or without preserving the privacy of the data, depending on the objectives. Federated learning is a more recent tool in this regard, which focuses on privacy preservation: this follows the concept of sharing the model parameters for a learning task rather than sharing the data, running algorithm constituents locally, and then globally optimizing parameters. There are other versions of privacy preserving information borrowing, such as personalized federated learning; the interest in such tasks can be evinced by the recent formation of a UN group to study computation measures enabling information borrowing under privacy guarantees. Heterogeneity is a bottleneck that significantly reduces the utility of information borrowing. In this paper, we identify the progress and gaps in current research regarding heterogeneity in information borrowing for federated learning. In addition to that, we also show using carefully constructed examples the limits of the federated learning paradigm when heterogeneity may be “washed out” when enough data are present. We show the limitations of current research in statistical learning and inference when heterogeneity is present.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms

We address the problem of automatically inferring the tense of events in Chinese text. We use a new corpus annotated with Chinese semantic tense information and other implicit Chinese linguistic information using a “distant annotation” method. We propose three improvements over a relatively strong baseline method ‐ a statistical learning method with extensive feature engineering. First, we add two sources of implicit linguistic information as features ‐ eventuality type and modality of an event, which are also inferred automatically. Second, we perform joint learning on semantic tense, eventuality type, and modality of an event. Third, we train artificial neural network models for this problem and compare its performance with feature-based approaches. Experimental results show considerable improvements on Chinese tense inference. Our best performance reaches 68.6% in accuracy, outperforming a strong baseline method.