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arXiv2022

A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources

This paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model {while studying Germany, the widest studied market in Europe}. The inter-dependencies are investigated in-depth and monitored over time, with particular emphasis on the tail behavior. To this end, suitable tail dependence measures are introduced to take into account a multivariate extreme scenario appropriately identified {through the} Kendall's distribution function. The empirical evidence demonstrates a strong association between electricity prices, renewable energy sources, and demand within a day and over the studied years. Hence, this analysis provides guidance for further and different incentives for promoting green energy generation while considering the time-varying dependencies of the involved variables

stat.AP↗

arXiv2022

SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs

The sparsity-restricted maximum likelihood estimator (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out SMLE in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and post-screening selection using SMLE with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of the package is illustrated on extensive numerical examples, where the promising performance of SMLE is well observed.

stat.OT↗

arXiv2022

Extending compositional data analysis from a graph signal processing perspective

Traditional methods for the analysis of compositional data consider the log-ratios between all different pairs of variables with equal weight, typically in the form of aggregated contributions. This is not meaningful in contexts where it is known that a relationship only exists between very specific variables (e.g.~for metabolomic pathways), while for other pairs a relationship does not exist. Modeling absence or presence of relationships is done in graph theory, where the vertices represent the variables, and the connections refer to relations. This paper links compositional data analysis with graph signal processing, and it extends the Aitchison geometry to a setting where only selected log-ratios can be considered. The presented framework retains the desirable properties of scale invariance and compositional coherence. An additional extension to include absolute information is readily made. Examples from bioinformatics and geochemistry underline the usefulness of thisapproach in comparison to standard methods for compositional data analysis.

stat.ME↗

arXiv2022

Estimating the Capacities of Function-as-a-Service Functions

Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications follow this model, where the application is decomposed into a set of fine-grained Function-as-a-Service (FaaS) functions. However, the obscurities of the underlying system infrastructure and dependencies between FaaS functions within the application pose a challenge for estimating the performance of FaaS functions. To characterize the performance of a FaaS function that is relevant for the user, we define Function Capacity (FC) as the maximal number of concurrent invocations the function can serve in a time without violating the Service-Level Objective (SLO). The paper addresses the challenge of quantifying the FC individually for each FaaS function within a serverless application. This challenge is addressed by sandboxing a FaaS function and building its performance model. To this end, we develop FnCapacitor - an end-to-end automated Function Capacity estimation tool. We demonstrate the functioning of our tool on Google Cloud Functions (GCF) and AWS Lambda. FnCapacitor estimates the FCs on different deployment configurations (allocated memory & maximum function instances) by conducting time-framed load tests and building various models using statistical: linear, ridge, and polynomial regression, and Deep Neural Network (DNN) methods on the acquired performance data. Our evaluation of different FaaS functions shows relatively accurate predictions, with an accuracy greater than 75% using DNN for both cloud providers.

cs.DC↗

arXiv2022

Effect of Measurement Errors on the Multivariate CUSUM CoDa Control Chart for the Manufacturing Process

Control charts, one of the main tools in Statistical Process Control (SPC), have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E's) are involved in the quality characteristic of interest. The authors explored the impact of a linear covariate error model on the multivariate cumulative sum (CUSUM) control charts for a specific kind of data known as compositional data(CoDa). The average run length ARL is used to assess the performance of the proposed chart. The results indicate that M.E's significantly affects the multivariate CUSUM-CoDa control charts. The authors have used the Markov chain method to study the impact of different involved parameters using four different cases for the variance-covariance matrix (i.e. uncorrelated with equal variances, negatively correlated with equal variances, uncorrelated with unequal variances, positively correlated with unequal variances). The authors concluded that the ARL of the multivariate CUSUM-CoDa chart increase with an increase in the value of error variance-covariance matrix, while the ARL decreases with an increase in the subgroup size m or the constant powering b. For the implementation of the proposal, two illustrated examples have been reported for multivariate CUSUM-CoDa control charts in the presence of M.E's. One deals with the manufacturing process of uncoated aspirin tablets, and the other is based on monitoring machines in the muesli manufacturing process.

stat.OT↗

arXiv2022

Efficient Volatility Estimation for Lévy Processes with Jumps of Unbounded Variation

Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than a decade. One of the most well-known and widely studied problems is that of estimation of the quadratic variation of the continuous component of an Itô semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a Lévy process, we construct a new rate- and variance-efficient estimator for a class of Lévy processes of unbounded variation, whose small jumps behave like those of a stable Lévy process with Blumenthal-Getoor index less than $8/5$. The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework.

econ.EM↗

arXiv2022

Efficient Privacy Preserving Logistic Regression for Horizontally Distributed Data

Internet of Things devices are expanding rapidly and generating huge amount of data. There is an increasing need to explore data collected from these devices. Collaborative learning provides a strategic solution for the Internet of Things settings but also raises public concern over data privacy. In recent years, large amount of privacy preserving techniques have been developed based on secure multi-party computation and differential privacy. A major challenge of collaborative learning is to balance disclosure risk and data utility while maintaining high computation efficiency. In this paper, we proposed privacy preserving logistic regression model using matrix encryption approach. The secure scheme is resilient to chosen plaintext attack, known plaintext attack, and collusion attack that could compromise any agencies in the collaborative learning. Encrypted model estimate is decrypted to provide true model results with no accuracy degradation. Verification phase is implemented to examine dishonest behavior among agencies. Experimental evaluations demonstrate fast convergence rate and high efficiency of proposed scheme.

cs.CR↗

arXiv2022

Mathematical models of confirmation bias

Confirmation bias is a cognitive bias that adversely affects management decisions, and mathematical modelling is an aid to its detailed understanding. Bias in opinion update about the value of a parameter is modelled here assuming that observations are discounted depending on their distance from prior opinion. The models allow belief persistence, attitude polarization, and the irrational primacy effect to be explored. A general framework for exploring large-sample properties of these models is given, and an attempt made to classify the models. An interesting result is that in some models the influence of an observation always increases with distance from the prior opinion, whereas in others observations greatly at odds with prior opinion are given very little weight. The models could be useful to those exploring these phenomena in detail.

stat.OT↗

arXiv2023

Real-time privacy preserving disease diagnosis using ECG signal

The rapid development in Internet of Medical Things (IoMT) boosts the opportunity for real-time health monitoring using various data types such as electroencephalography (EEG) and electrocardiography (ECG). Security issues have significantly impeded the e-healthcare system implementation. Three important challenges for privacy preserving system need to be addressed: accurate diagnosis, privacy protection without compromising accuracy, and computation efficiency. It is essential to guarantee prediction accuracy since disease diagnosis is strongly related to health and life. By implementing matrix encryption method, we propose a real-time disease diagnosis scheme using support vector machine (SVM). A biomedical signal provided by the client is diagnosed such that the server does not get any information about the signal as well as the final result of the diagnosis while the proposed scheme also achieves confidentiality of the SVM classifier and the server's medical data. The proposed scheme has no accuracy degradation. Experiments on real-world data illustrate the high efficiency of the proposed scheme. It takes less than 1 second to derive the disease diagnosis result using a device with 4Gb RAMs, suggesting the feasibility to implement real-time privacy preserving health monitoring.

cs.CR↗

arXiv2022

Mr. Bayes and the classics: a suggested interpretation

The main hypothesis about Thomas Bayes's intentions to write his famous Essay on probability is that he wanted to refute the arguments of David Hume against the reliability of the occurrence of miracles, published in 1748. In this paper we argue that it was not Bayes's intention to rebut Hume but that his interest on the "inverse problem" came about as result of his study of the second edition of Abraham De Moivre's book, The Doctrine of Chances, published in 1738. A possible communication of Bayes's breakthrough might have annoyed De Moivre, leading to a response written for Bayes in the third edition of De Moivre's book, published in 1756. Among other points, the response claims that De Moivre was the first to solve the mentioned inverse problem. Under this perspective Richard Price's letter, written as preface to Bayes's essay, has a new interpretation, appearing also as a defense of Bayes premiership on a satisfactory or proper solution.

stat.OT↗

arXiv2022

Group testing via residuation and partial geometries

The motivation for this paper comes from the ongoing SARS-CoV-2 Pandemic. Its goal is to present a previously neglected approach to non-adaptive group testing and describes it in terms of residuated pairs on partially ordered sets. Our investigation has the advantage, as it naturally yields an efficient decision scheme (decoder) for any given testing scheme. This decoder allows to detect a large amount of infection patterns. Apart from this, we devise a construction of good group testing schemes that are based on incidence matrices of finite partial linear spaces. The key idea is to exploit the structure of these matrices and make them available as test matrices for group testing. These matrices may generally be tailored for different estimated disease prevalence levels. As an example, we discuss the group testing schemes based on generalized quadrangles. In the context at hand, we state our results only for the error-free case so far. An extension to a noisy scenario is desirable and will be treated in a subsequent account on the topic.

stat.OT↗

arXiv2022

Temporal Properties of Vaccine Effectiveness Measures in Presence of Multiple Pathogen Variants and Multiple Vaccines

Vaccine effectiveness (VE) is typically defined as incidence rate ratio, cumulative-risk ratio, or odds ratio. The VE based on incidence rate ratio is known to be time-invariant over the study period for leaky action vaccines and, the VE based on cumulative-risk ratio is time-invariant for all-or-none action vaccines. Consequently, these VE measures are recommended as appropriate measures of VE for leaky and all-or-none vaccines, respectively. However, in diseases with multiple pathogen variants and multiple vaccines, investigators may also be interested in variant-specific VE of a vaccine, the relative VE of a vaccine against two variants, or the relative VE of different vaccines against a given variant. In this multi-variant and multi-vaccine scenario, the temporal properties of the aforementioned VE measures have not been studied entirely yet. Furthermore, no general-purpose sample size calculator is available for either studies that intend to estimate variant-specific VE or relative VE. As a solution, we define variant-specific and relative VE measures while accounting for multiple competing pathogen variants. We then propose a generic mode of action for all-or-none vaccines in a multi-variant setting. Subsequently, we evaluate the conditions and the extent to which various VE measures can be time-varying. We show that every VE measure is time-varying for all-or-none action vaccines in a multi-variant and multi-vaccine scenario. For leaky vaccines, we show that all measures other than those based on incidence rate ratios are time-varying. We discuss the practical implications of these results on VE studies in the context of the commonly used cohort, cumulative case-control, and test-negative study designs. Lastly, for the multi-variant and multi-vaccine scenario, we implement sample size calculations for both variant-specific and relative VE in an R package and an online calculator.

stat.AP↗

arXiv2022

Modelling and forecasting patient recruitment in clinical trials with patients' dropout

This paper focuses on statistical modelling and prediction of patient recruitment in clinical trials accounting for patients dropout. The recruitment model is based on a Poisson-gamma model introduced by Anisimov and Fedorov (2007), where the patients arrive at different centres according to Poisson processes with rates viewed as gamma-distributed random variables. Each patient can drop the study during some screening period. Managing the dropout process is of a major importance but data related to dropout are rarely correctly collected. In this paper, a few models of dropout are proposed. The technique for estimating parameters and predicting the number of recruited patients over time and the recruitment time is developed. Simulation results confirm the applicability of the technique and thus, the necessity to account for patients dropout at the stage of forecasting recruitment in clinical trials.

stat.ME↗

arXiv2022

Edge coherence in multiplex networks

This paper introduces a nonparametric framework for the setting where multiple networks are observed on the same set of nodes, also known as multiplex networks. Our objective is to provide a simple parameterization which explicitly captures linear dependence between the different layers of networks. For non-Euclidean observations, such as shapes and graphs, the notion of "linear" must be defined appropriately. Taking inspiration from the representation of stochastic processes and the analogy of the multivariate spectral representation of a stochastic process with joint exchangeability of Bernoulli arrays, we introduce the notion of edge coherence as a measure of linear dependence in the graph limit space. Edge coherence is defined for pairs of edges from any two network layers and is the key novel parameter. We illustrate the utility of our approach by eliciting simple models such as a correlated stochastic blockmodel and a correlated inhomogeneous graph limit model.

stat.ME↗

arXiv2022

Exploring the effects of activity-preserving time dilation on the dynamic interplay of airborne contagion processes and temporal networks using an interaction-driven model

Contacts' temporal ordering and dynamics are crucial for understanding the transmission of infectious diseases. We introduce an interaction-driven model of an airborne disease over contact networks. We demonstrate our interaction-driven contagion model, instantiated for COVID-19, over history-maintaining random temporal networks and real-world contacts. We use it to evaluate temporal, spatiotemporal, and spatial social distancing policies. We find that a spatial distancing policy is mainly beneficial at the early stages of a disease. We then continue to evaluate temporal social distancing, that is, timeline dilation that maintains the activity potential. We expand our model to consider the exposure to viral load, which we correlate with meetings' duration. Using real-life contact data, we demonstrate the beneficial effect of timeline dilation on overall infection rates. Our results demonstrate that given the same transmission level, there is a decrease in the disease's infection rate and overall prevalence under timeline dilation conditions. We further show that slow-spreading pathogens (i.e., require more prolonged exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This is surprising since slower pathogens follow paths that include longer meetings, while faster pathogens can potentially follow paths that include shorter meetings, which are more common. Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes.

cs.SI↗

arXiv2022

Revisiting the secondary climate attributes for transportation infrastructure management: A Redux and Update for 2020

Environmental conditions in various regions can have a severely negative impact on the longevity and durability of the civil engineering infrastructures. In 2018, a published paper used 1971 to 2010 NOAA data from the contiguous United States to examine the temporal changes in secondary climate attributes (freeze-thaw cycles and freeze index) using the climate normals from two time windows, 1971-2000 and 1981-2010. That paper investigated whether there have been statistically significant changes in climate attribute levels across the two time windows, and used GIS-based interpolation methods to develop isarithmic maps of the climate attributes to facilitate their interpretation and application. This paper updates that study. In this study, we use NOAA climatic data from 1991 to 2020 to construct continuous surface maps showing the values of the primary and secondary climate attributes at the 48 continental states. The new maps provide an updated picture of the freeze index values and freeze-thaw cycles for the most recent climate condition. These new values provide a better picture of the freezing season characteristics of the United States, and will provide information necessary for better winter maintenance procedures and infrastructure design to accommodate regional climate differences.

stat.OT↗

arXiv2022

Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE=6.0) and smallest for QMD (RE=1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.

stat.AP↗

arXiv2022

Predicting Cricket Outcomes using Bayesian Priors

This research has developed a statistical modeling procedure to predict outcomes of future cricket tournaments. Proposed model provides an insight into the application of stratified survey sampling to the team selection pattern by incorporating individual players' performance history coupled with Bayesian priors not only against a particular opposition but also against any cricket playing nation - full member of International Cricket Council (ICC). A case study for the next ICC cricket world cup 2023 in India is provided, predictions are obtained for all participating teams against one another, and simulation results are discussed. The proposed statistical model is tested on 2020 Indian Premier League (IPL) season. The model predicted the top three finishers of IPL 2020 correctly, including the winners of the tournament, Mumbai Indians, and other positions with reasonable accuracy. The method can predict probabilities of winning for each participating team. This method can be extended to other cricket tournaments as well.

stat.AP↗