Given Level Of Confidence Research Articles

Spatiotemporal Analysis of Ozone Concentration Using Semi-Functional Partial Linear Models

As weather warms up in China, ozone pollution rises to the top of the list of air pollutants. In this research, we examine the spatiotemporal variability of particulate matter components using contemporary functional data analysis techniques. The technique models the yearly pollutant profiles to describe their dynamic behavior over time and location. These cutting-edge methods offer dimension reduction for better data display and permit us to forecast annual profiles for locations and years for which data are lacking. In order to accurately estimate hourly ozone concentrations for 12 stations in China over two years (2015-2016), this study set out to showcase the best prediction models currently available. To accurately predict Ozone concentration, several methods are used, including Kernel Functional Classical Estimation (KFCE), Kernel Functional Quantile estimation (KFQE), Semi-Partial Linear Functional Classical Estimation (SPLFCE), Semi-Partial Linear Functional Quantile Estimation (SPLFQE), and Semi-Partial Linear Functional Expectile Estimation (SPLFEE). These functional models were chosen based on their ability to establish a forecast region with a given level of confidence. In terms of prediction accuracy, we may conclude that the Semi-Partial linear models outperform conventional models.

Repeated home drinking water sampling to improve detection of particulate lead spikes: a simulation study

BackgroundLead can be present in drinking water in soluble and particulate forms. The intermittent release of lead particulates in drinking water can produce highly variable water lead levels (WLLs) in individual homes, a health concern because both particulate and soluble lead are bioavailable. More frequent water sampling would increase the likelihood of identifying sporadic lead “spikes,” though little information is available to aid in estimating how many samples are needed to achieve a given degree of sensitivity to spike detection.ObjectiveTo estimate the number of rounds of tap water sampling needed to determine with a given level of confidence that an individual household is at low risk for the intermittent release of lead particulates.MethodsWe simulated WLLs for 100,000 homes on 15 rounds of sampling under a variety of assumptions about lead spike release. A Markovian structure was used to describe WLLs for individual homes on subsequent rounds of sampling given a set of transitional probabilities, in which homes with higher WLLs at baseline were more likely to exhibit a spike on repeated sampling.ResultsAssuming 2% of homes had a spike on the first round of sampling and a mid-range estimate of transitional probabilities, the initial round of sampling had a 6.4% sensitivity to detect a spike. Seven rounds of sampling would be needed to increase the sensitivity to 50%, which would leave unrecognized the more than 15,000 homes that intermittently exhibit spikes.SignificanceFor assessing household risk for lead exposure through drinking water, multiple rounds of water sampling are needed to detect the infrequent but high spikes in WLLs due to particulate release. Water sampling procedures for assessment of lead exposure in individual homes should be modified to account for the infrequent but high spikes in WLL.ImpactIt has been known for decades that intermittent “spikes” in water lead occur due to the sporadic release of lead particulates. However, conventional water sampling strategies do not account for these infrequent but hazardous events. This research suggests that current approaches to sampling tap water for lead testing identify only a small fraction of homes in which particulate spikes occur, and that sampling procedures should be changed substantially to increase the probability of identifying the hazard of particulate lead release into drinking water.

On the Construction of Kinematic Confidence Ellipsoids for Uncertain Spatial Displacements.

This paper deals with the problem of estimating confidence regions of a set of uncertain spatial displacements for a given level of confidence or probabilities. While a direct application of the commonly used statistic methods to the coordinates of the moving frame is straight-forward, it is also the least effective in that it grossly overestimate the confidence region. Based on the dual-quaternion representation, this paper introduces the notion of the kinematic confidence ellipsoids as an alternative to the existing method called rotation and translation confidence limit (RTCL). An example is provided to demonstrate how the kinematic confidence ellipsoids can be computed.

Testing for the rank of a covariance operator

How can we discern whether the covariance operator of a stochastic process is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional data, which require low-dimensional representations whether by functional PCA or other methods. The difficulty is that the determination is to be made on the basis of i.i.d. replications of the process observed discretely and with measurement error contamination. This adds a ridge to the empirical covariance, obfuscating the underlying dimension. We build a matrix-completion inspired test statistic that circumvents this issue by measuring the best possible least square fit of the empirical covariance’s off-diagonal elements, optimised over covariances of given finite rank. For a fixed grid of sufficiently large size, we determine the statistic’s asymptotic null distribution as the number of replications grows. We then use it to construct a bootstrap implementation of a stepwise testing procedure controlling the familywise error rate corresponding to the collection of hypotheses formalising the question at hand. Under minimal regularity assumptions, we prove that the procedure is consistent and that its bootstrap implementation is valid. The procedure circumvents smoothing and associated smoothing parameters, is indifferent to measurement error heteroskedasticity, and does not assume a low-noise regime. An extensive simulation study reveals an excellent practical performance, stably across a wide range of settings and the procedure is further illustrated by means of two data analyses.

POLICING FORENSIC EVIDENCE

POLICING FORENSIC EVIDENCE

Portfolio value-at-risk estimation for spot chartering decisions under changing trade patterns: A copula approach.

Evolving geopolitical relationships between countries (especially between China and the United States) in recent years have highlighted dynamically changing trade patterns across the globe, all of which elevate risk and uncertainty for transport service providers. In order to mitigate risks, shipowners and operators must be able to estimate risks appropriately; one potentially promising method of doing so is through the value-at-risk (VaR) method. VaR describes the worst loss a portfolio is likely to sustain, which will not be exceeded over a target time horizon at a given level of confidence. This article proposes a copula-based GARCH model to estimate the joint multivariate distribution, which is a key component in VaR estimation. We show that the copula model can capture the VaR more successfully, as compared with the traditional method of calculation. As an empirical study, the expected portfolio VaR is examined when a shipowner chooses among Panamax soybean trading routes under a condition of reduced trade volumes between the United States and China due to the ongoing trade turmoil. This study serves as one of the very few papers in the literature on shipping portfolio VaR analysis. The results have significant implications for shipowners regarding fleet repositioning, decision making, and risk management.

Optimizing experimental design in neutron reflectometry.

Using the Fisher information (FI), the design of neutron reflectometry experiments can be optimized, leading to greater confidence in parameters of interest and better use of experimental time [Durant, Wilkins, Butler & Cooper (2021). J. Appl. Cryst. 54, 1100-1110]. In this work, the FI is utilized in optimizing the design of a wide range of reflectometry experiments. Two lipid bilayer systems are investigated to determine the optimal choice of measurement angles and liquid contrasts, in addition to the ratio of the total counting time that should be spent measuring each condition. The reduction in parameter uncertainties with the addition of underlayers to these systems is then quantified, using the FI, and validated through the use of experiment simulation and Bayesian sampling methods. For a 'one-shot' measurement of a degrading lipid monolayer, it is shown that the common practice of measuring null-reflecting water is indeed optimal, but that the optimal measurement angle is dependent on the deuteration state of the monolayer. Finally, the framework is used to demonstrate the feasibility of measuring magnetic signals as small as 0.01 μB per atom in layers only 20 Å thick, given the appropriate experimental design, and that the time to reach a given level of confidence in the small magnetic moment is quantifiable.

Benchmark test for calculated default probability values derived from the application of rating-based models

Subject. This article deals with the issues of validation of the consistency of rating-based model forecasts. Objectives. The article aims to provide developers and validators of rating-based models with a practical fundamental test for benchmarking study of the estimated default probability values obtained as a result of the application of models used in the rating system. Methods. For the study, I used the classical interval approach to testing of statistical hypotheses focused on the subject area of calibration of rating systems. Results. In addition to the generally accepted tests for the correspondence of the predicted probabilities of default of credit risk objects to the historically realized values, the article proposes a new statistical test that corrects the shortcomings of the generally accepted ones, focused on "diagnosing" the consistency of the implemented discrimination of objects by the rating model. Examples of recognizing the reasons for a negative test result and negative consequences for lending are given while maintaining the current settings of the rating model. In addition to the bias in the assessment of the total frequency of defaults in the loan portfolio, the proposed method makes it possible to objectively reveal the inadequacy of discrimination against borrowers with a calibrated rating model, diagnose the “disease” of the rating model. Conclusions and Relevance. The new practical benchmark test allows to reject the hypothesis about the consistency of assessing the probability of default by the rating model at a given level of confidence and available historical data. The test has the advantage of practical interpretability based on its results, it is possible to draw a conclusion about the direction of the model correction. The offered test can be used in the process of internal validation by the bank of its own rating models, which is required by the Bank of Russia for approaches based on internal ratings.

A probabilistic approach to evaluate dynamic and static strength of quasi-brittle materials through high-rate testing

Results of mechanical tests, especially in dynamics, obtained under similar input conditions always have a statistical scatter. Currently, there is no generally accepted engineering approach to the correct assessment of critical stresses for a material under dynamic loads. For example, there is no clear understanding of how many specimens need to be tested to estimate a specific ultimate stress for a specific impact rate. The present study enhances the applicability of the randomized algorithm of Sign-Perturbed Sums to solve the mentioned problem of the dynamic strength evaluation under typical restrictions on experimental results such as few numbers of data points with unknown random noises. The combination of the incubation time criteria and this new probabilistic approach permits to estimate accurately material strength parameters: the critical failure stress and the incubation time with a given level of confidence. Comparative analysis of developing approach and different modifications of the scaling law obtained by Kimberly and Ramesh showed that both models are well agreed with empirically observed data. Experimental data of dynamic test on split Hopkinson pressure bars and a drop tower setup for different quasi-brittle materials are taken to demonstrate advantages of the new method. It is shown that incubation time of failure and critical stress values of certain materials can be estimated with a high level of confidence. These results can be used to optimize experimental studies and develop new standards for dynamic testing of materials.

Метод обробки вiдеодaних з можливістю їх захисту після квaнтувaння

The subject of research in the article is the video processing processes based on the JPEG platform for data transmission in the information and telecommunication network. The aim is to build a method for processing a video image with the possibility of protecting it at the quantization stage with subsequent arithmetic coding. That will allow, while preserving the structural and statistical regularity, to ensure the necessary level of accessibility, reliability, and confidentiality when transmitting video data. Task: research of known methods of selective video image processing with the subsequent formalization of the video image processing procedure at the quantization stage and statistical coding of significant blocks based on the JPEG platform. The methods used are an algorithm based on the JPEG platform, methods for selecting significant informative blocks, arithmetic coding. The following results were obtained. A method for processing a video image with the possibility of its protection at the stage of quantization with subsequent arithmetic coding has been developed. This method will allow, while preserving the structural and statistical regularity, to fulfill the set requirements for an accessible, reliable, and confidential transmission of video data. Ensuring the required level of availability is associated with a 30% reduction in the video image volume compared to the original volume. Simultaneously, the provision of the required level of confidence is confirmed by an estimate of the peak signal-to-noise ratio for an authorized user, which is dB. Ensuring the required level of confidentiality is confirmed by an estimate of the peak signal-to-noise ratio in case of unauthorized access, which is equal to dB. Conclusions. The scientific novelty of the results obtained is as follows: for the first time, two methods of processing video images at the quantization stage have been proposed. The proposed technologies fulfill the assigned tasks to ensure the required level of confidentiality at a given level of confidence. Simultaneously, the method of using encryption tables has a higher level of cryptographic stability than the method of using the key matrix. It is due to a a more complex mathematical apparatus. Which, in turn, increases the time for processing the tributes. To fulfill the requirement of data availability, it is proposed to use arithmetic coding for info-normative blocks, which should be more efficient compared with the methods of code tables. So, the method of using the scoring tables has greater cryptographic stability, and the method of using the matrix-key has higher performance. Simultaneously, the use of arithmetic coding will satisfy the need for accessibility by reducing the initial volume.

Statistical stopping criteria for automated screening in systematic reviews

Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents. However, current approaches lack robust stopping criteria, so that reviewers do not know when they have seen all or a certain proportion of relevant documents. This means that such systems are hard to implement in live reviews. This paper introduces a workflow with flexible statistical stopping criteria, which offer real work reductions on the basis of rejecting a hypothesis of having missed a given recall target with a given level of confidence. The stopping criteria are shown on test datasets to achieve a reliable level of recall, while still providing work reductions of on average 17%. Other methods proposed previously are shown to provide inconsistent recall and work reductions across datasets.

An Optimal Phase Arrangement of Distribution Transformers under Risk Assessment

This paper presents a phase arrangement procedure for distribution transformers to improve system unbalance and voltage profile of distribution systems, while considering the location and uncertainties of the wind turbine (WT) and photovoltaics (PV). Based on historical data, the Monte Carlo method is used to calculate the power generation value-at-risk (VAR) of WTs/PVs installed under a given level of confidence. The main target of this paper is to reduce the line loss and unbalance factor during 24-hour intervals. Assessing the various confidence levels of risk, a feasible particle swarm optimization (FPSO) is proposed to solve the optimal location of WTs/PVs installed and transformer load arrangement. A three-phase power flow with equivalent current injection (ECI) is analyzed to demonstrate the operating efficiency of the FPSO in a Taipower feeder. Simulation results will support the planner in the proper location of WTs/PVs installed to reduce system losses and maintain the voltage profile. They can also provide more risk information for handing uncertainties when the renewable energy is connected to the distribution system.

Картографування радіоактивного забруднення із заданим рівнем довіри

Standard approaches to the construction of maps of radioactive contamination do not provide errors in map data, so such maps do not, in fact, guarantee the accuracy of the map information. In this paper, based on the fact that the characteristics of radioactive contamination at a particular point in the territory have a lognormal probability distribution, a methodology for creating maps with a guaranteed confidence level of the provided information has been proposed and tested. There are considered two ways of creating maps, based on the results of "direct" measurements of radioactive contamination characteristics and in the combination of "direct" and "indirect" measurements of values statistically related to the mapping characteristic. The approaches and use of kriging methods proposed in the article allow to create maps with a given level of confidence and, accordingly, to take into account the risks caused by the uncertainty of measurements of radioactive contamination characteristics and uncertainty of their approximation.

Uncertainty in automated valuation models: Error-based versus model-based approaches

ABSTRACT Point estimates from Automated Valuation Models (AVMs) represent the most likely value from a distribution of possible values. The uncertainty in the point estimate – the width of the range of possible values at a given level of confidence – is a critical piece of the AVM output, especially in collateral and transactional situations. Estimating AVM uncertainty, however, remains highly unstandardised in both terminology and methods. In this paper, we present and compare two of the most common approaches to estimating AVM uncertainty – model-based and error-based prediction intervals. We also present a uniform language and framework for evaluating the calibration and efficiency of uncertainty estimates. Based on empirical tests on a large, longitudinal dataset of home sales, we show that model-based approaches outperform error-based ones in all but cases with very highest confidence level requirements. The differences between the two methods are conditioned on model class, geographic data partitions and data filtering conditions.

Determination of artificial recharge location using analytic hierarchy process and Dempster–Shafer theory

Groundwater is an essential and major source of water in semi-arid and arid regions, and thus, determining potential locations for groundwater recharge, via prompt and accurate techniques, is necessary. The aim of the research is finding the best artificial recharge location for injecting the excess surface water into aquifer in Shiraz watershed comparing fuzzy-AHP method and Dempster–Shafer theory (DST). The former employs membership functions to determine the importance of each factor in artificial recharge and generates fuzzy maps through analytic hierarchy process (AHP), whereas the later takes the level of confidence, management strategy and geographic conditions into account. The outcomes of this study demonstrate the applicability of the proposed methods for the artificial recharge location prediction. The DST is among the foremost tools, which produces an artificial recharge map at a given level of confidence. For Shiraz watershed, three levels of confident of 99.5%, 99%, and 95% were applied and the results were compared with fuzzy-AHP method. The fuzzy-AHP method identified ~ 19.76% and 6.02% of the study area as highly suitable and unsuitable classes, respectively. While in the DST, the method identified ~ 32% of the area as moderately suitable. The comparison of the two methods to determine the best location for artificial recharge showed that the fuzzy-AHP method had a lower accuracy than DST in determining suitable locations for artificial recharge. Additionally, the DST method can generate several maps with different confidence levels that suit the economic conditions and importance of the study area.

Критерій перевірки гіпотези про значення параметра Хюрста дробового броунівського руху

In this paper the statistical hypothesis testing task about the value of the Hurst parameter of fractional Brownian motion {ξ(t), t ∈ (0, 1)} is considered. The estimation problem for the Hurst parameter of a fractional Brownian motion or of Self Similarity Index plays an important role in statistics of stochastic processes. The proposed criterion for testing hypotheses about the value of the Hurst parameter is based on the Baxter sums method. The applications of the Baxter sums method for stochastic processes and fields allows us to build the strongly consistent estimators and to construct the non-asymptotic confidence intervals without the use of classical limit theorems in many models. We want to construct criterion for testing hypotheses about the value of the Hurst parameter α of a fractional Brownian motion H0 : α = α0 with the alternative H1 : α 6= α0, where α0 0 is constructed by using Baxter statistic, the elements of theory of Orlicz space and some inequality for quadratic forms of Gaussian random variables. The null hypotheses is not rejected, if −xp xp} ≤ p. The inequality sets the set of values for variable ˆαn, which will not lead to the rejection of a specific null hypothesis about α = α0. This set of values will be the region of acceptance for a given level of confidence p ∈ (0, 1).

Model confidence bounds for variable selection.

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool-the model uncertainty curve (MUC)-is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.

Minimum Detectable Changes Associated with Tissue Dielectric Constant Measurements as Applicable to Assessing Lymphedema Status

Background: Tissue dielectric constant (TDC) measurements are increasingly being used as a tool to help characterize lymphedema features, detect its presence, and assess treatment related changes. Although the underlying physics of this technology has been well described in the literature, there has been little systematic study of in vivo reliability aspects. A central unanswered question is the minimal detectable change (MDC) that, with a given level of confidence, may be ascribed to this technology. Our goal was to address this issue using test-retest measurements from which intraclass correlations coefficients (ICC) and MDC could be estimated. Methods and Results: Forty volunteers (20 females) aged 19-61 years with body mass indices of 14.7-47 kg/m2 and body fat percentages of 12.0%-48.9% were evaluated. Two measurers (M1 and M2) used two different TDC measuring devices (multiprobe and compact) to measure TDC in triplicate sequentially and bilaterally at three locations; anterior forearm, hand palmar mid-thenar eminence, and dorsum mid-web. These measurements were made by each measurer twice constituting test-retest values (T1 and T2). From these measurements ICC2,1 and MDC at 95% confidence were determined for each site and probe for absolute TDC values and for inter-side ratios. MDC values for absolute TDC ranged from 2 to 9 TDC units, and for inter-side ratios ranged from 5.3% to 8.0% depending on site and probe. ICC2,1 values ranged from 0.765 to 0.982. Conclusions: The MDC values herein documented may be used to provide guidance to aid interpretation of measured TDC changes or differences in a clinical environment.

A fast and efficient conformal regressor with regularized extreme learning machine

Abstract A conformal regressor combines conformal prediction and a traditional regressor for point predictions. It produces a valid prediction interval for a new testing input such that the probability of the target output being not included in the prediction interval is not more than a preset significance level. Although conformal prediction is both theoretically and empirically valid, one main drawback of the existing conformal regressors is their computational inefficiency. This paper proposes a novel fast and efficient conformal regressor named LW-JP-RELM, with combination of the local-weighted jackknife prediction (LW-JP), a new variant of conformal prediction, and the regularized extreme learning machine (RELM). The development of our learning algorithm is important both for the applications of extreme learning machine and conformal prediction. On the one hand, LW-JP-RELM complements ELM with interval predictions that satisfy a given level of confidence. On the other hand, the underlying learning process and the outstanding learning ability of RELM make LW-JP-RELM a very fast and informationally efficient conformal regressor. In the experiments, the empirical validity and informational efficiency of our method were compared to those of the state-of-art on 20 public data sets and the results confirmed that LW-JP-RELM is a competitive and promising conformal regressor.

Power Electronics Modeling and Design: Using Parametric and Model-Form Uncertainty Quantification to Assess Predictive Accuracy of Power Converter Models

This article presents an enhanced modeling framework for the design of power electronics converters and systems. This methodology provides the means to assess and quantify the predictive accuracy of power converter models, a capability that is attained by the proper identification, characterization, and quantification of the different, and often numerous, sources of uncertainty in the modeling and simulation process. For illustrative purposes, an in-depth example is presented using a power electronics building block (PEBB) as the modeling test bed, demonstrating how, through this formal modeling procedure-and taking into consideration the uncertainties in the system-the model in question can predict the variable of interest within a known range with a given level of confidence, without requiring heuristic safety factors. This predicted range can be used as the required design margin when the selected output is a design variable, evincing the inherent potential of having a less conservative and more reliable power converter using the design methodology with uncertainty quantification.