Statistical learning method of discontinuous functions using simultaneous recurrent networks

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.

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.

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.

Machine learning methods are increasingly applied for medical data analysis to reduce human efforts and improve our understanding of disease propagation. When the data is complicated and unstructured, shallow learning methods may not be suitable or feasible. Deep learning neural networks like multilayer perceptron (MLP) and convolutional neural network (CNN), have been incorporated in medical diagnosis and prognosis for better health care practice. For a binary outcome, these learning methods directly output predicted probabilities for patient’s health condition. Investigators still need to consider appropriate decision threshold to split the predicted probabilities into positive and negative regions. We review methods to select the cut-off values, including the relatively automatic methods based on optimization of the ROC curve criteria and also the utility-based methods with a net benefit curve. In particular, decision curve analysis (DCA) is now acknowledged in medical studies as a good complement to the ROC analysis for the purpose of decision making. In this paper, we provide the R code to illustrate how to perform the statistical learning methods, select decision threshold to yield the binary prediction and evaluate the accuracy of the resulting classification. This article will help medical decision makers to understand different classification methods and use them in real world scenario.

With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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.

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

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.

In recent years, with the rapid development of mass spectrometry, there are more and more cases of finding biomarkers by proteomics for clinical diagnosis and treatment, and artificial intelligence is gradually replacing traditional statistical learning methods, but the process of finding biomarkers is not yet uniform and perfect, and there are gaps in validation. With this aim, this study proposes a pipeline for the analysis of biomarkers based on data independent acquisition(MS-BioAP). MS-BioAP includes three modules: mass spectrometry protein identification module, differential protein analysis module, and classification model building module. Combining statistical learning and artificial intelligence methods to complete the biomarker search, we also propose a target association scoring algorithm(TAScore), use the large amount of prior knowledge in the knowledge graph to complete the biomarker validation, and create a classification model based on the selected biomarkers. We conducted experiments using a clinical stress response mass spectrometry dataset and the final selected biomarkers achieved an accuracy of 1.0 in the classification model with both high sensitivity and specificity. The analytical pipeline is complete and comprehensive, with clinical implications. In the future, we will continue to optimize the analytical pipeline and validate this analytical pipeline with additional datasets.

A literature survey of corporate failure prediction models

This study shows selected statistical learning models for prediction VO 2 max based on 20 m shuttle run and selected somatic parameters. The research were conducted on 80 male physical education students (age: 21.4±1.6). Predictive models were determined from 65 patterns and tested using 15 patterns. The 9 variables were used to build the models, including one dependent variable and 8 independent variables. The prediction methods used were as follows: ordinary least square regression, Ridge regression, Lasso regression, multilayer perceptron and network with radial basis functions. The research show that the model with the least error of prediction is the network with radial basis functions generating the RMSE CV = 4.04 error. The best method for new data generate an error on the level of 3.04 compared to the real values.

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.

The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offers self-contained coverage of semisupervised methods that includes background material on supervised and unsupervised learning. The book presents a brief history of semisupervised learning and its place in the spectrum of learning methods before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, support vector machines (SVMs), and the null-category noise model. In addition, the book covers clustering, the expectation-maximization (EM) algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation as well as a detailed discussion of spectral methods. Taking an intuitive approach to the material, this lucid book facilitates the application of semisupervised learning methods to natural language processing and provides the framework and motivation for a more systematic study of machine learning.

Missing data imputation using statistical and machine learning methods in a real breast cancer problem