Coverage Interval Research Articles

Conformalized temporal convolutional quantile regression networks for wind power interval forecasting

Wind power interval prediction is an effective technique for quantifying forecasting uncertainty caused by the intermittent and fluctuant characteristics of wind energy. Valid coverage and short interval length are the two most critical targets in interval prediction to attain reliable and accurate information, providing effective support for decision-makers to better control the risks in the power planning. This paper proposes a novel interval prediction approach named conformalized temporal convolutional quantile regression networks (CTCQRN) which combines the conformalized quantile regression (CQR) algorithm with a temporal convolutional network (TCN), without making any distributional assumptions. The proposed model inherits the advantages of quantile regression and conformal prediction that is fully adaptive to heteroscedasticity implicated in data, and meets the theoretical guarantee of valid coverage. As opposed to conventional RNN-based approaches, the adopted TCN architecture frees from suffering iterative propagation and gradient vanishing/explosion, and can handle very long sequences in a parallel manner. Case studies on two different geographical wind power datasets show that the proposed model has a distinct edge over benchmark models in goals of valid coverage and narrow interval bandwidth, which can help to ensure the economic and secure operation of the electric power system.

Acute Treatment Effects on GFR in Randomized Clinical Trials of Kidney Disease Progression.

Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. The mean acute effect across all studies was -0.21 ml/min per 1.73 m2 (95% confidence interval, -0.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, -2.50 to +2.08 ml/min per 1.73 m2). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD.

Analysis of gamma-ray spectra with spectral unmixing — Part I: Determination of the characteristic limits (decision threshold and statistical uncertainty) for measurements of environmental aerosol filters

Poisson-statistics based spectral unmixing has been shown to be an efficient analysis tool for the radionuclides activity estimation from gamma-ray spectrometry measurements. However, the calculation of the corresponding characteristic limits has not been investigated so far. In this paper, we present the quantification of the decision threshold and the limits of the coverage interval for the metrological use of such spectral unmixing algorithms. The proposed approach is evaluated and validated with simulated spectra of HPGe and NaI measurements by comparing the results to characteristic limits calculated from Monte-Carlo simulations. We focus particularly on the validation of the method for the metrological analysis of environmental measurements, for which the low-level activity quantification requires an accurate characteristic limits determination. Along with the instrument calibration, we establish a metrological analysis tool by using the spectral unmixing algorithm for environmental aerosol filters measured by gamma-ray spectrometry.

Theoretical aspects on the use of single-time-point dosimetry for radionuclide therapy

Objective. This study considers the error distributions for time-integrated activity (TIA) of single-time-point (STP) methods for patient-specific dosimetry in radionuclide therapy. Approach. The general case with the same pharmaceutical labelled with different radionuclides for imaging and therapy are considered for a mono-exponential time-activity curve. Two methods for STP dosimetry, both based on the combination of one activity estimate with the population-mean effective decay constant, are investigated. The cumulative distribution functions (CDFs) and the probability density functions for the two methods are analytically derived for arbitrary distributions of the biological decay constant. The CDFs are used for determining 95% coverage intervals of the relative errors for different combinations of imaging time points, physical decay constants, and relative standard deviations of the biological decay constant. Two examples, in the form of kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy and tumour dosimetry for Na[131I]I therapy for thyroid cancer with dosimetry based on imaging of Na[124I]I, are also studied in more detail with analysis of the sensitivity with respect to errors in the mean biological decay constant and to higher moments of the distribution. Main results. The distributions of the relative errors are negatively skewed, potentially leading to the situation that some TIA estimates are highly underestimated even if the majority of estimates are close to the true value. Significance. The main limitation of the studied STP dosimetry methods is the risk of large underestimations of the TIA.

Novel pathway for the sonochemical synthesis of silver nanoparticles with near-spherical shape and high stability in aqueous media

The present study shows the development of a novel sonochemical synthesis pathway of sub-15 nm silver nanoparticles (AgNPs) with quasi-spherical shape and high stability in aqueous suspension. Different analytical techniques such as on-line UV–Vis spectroscopy, Atomic Force Microscopy (AFM), and Transmission Electron Microscopy (TEM) were complementarily used to characterize the evolution of the properties of AgNPs synthesized with this new route. Furthermore, different centrifugation conditions were studied to establish a practical, simple and straightforward purification method. Particle size was determined by TEM employing two different deposition methods, showing that purified AgNPs have a size of 8.1 nm ± 2.4 nm with a narrow dispersion of the size distribution (95% coverage interval from 3.4 to 13 nm). Critical information of the shape and crystalline structure of these sub-15 nm AgNPs, provided by shape descriptors (circularity and roundness) using TEM and high resolution (HR)-TEM measurements, confirmed the generation of AgNPs with quasi-spherical shapes with certain twin-fault particles promoted by the high energy of the ultrasonic treatment. Elemental analysis by TEM-EDS confirmed the high purity of the sub-15 nm AgNPs, consisting solely of Ag. At the optical level, these AgNPs showed a bandgap energy of (2.795 ± 0.002) eV. Finally, the evaluation of the effects of ultraviolet radiation (UVC: 254 nm and UVA: 365 nm) and storage temperature on the spectral stability revealed high stability of the optical properties and subsequently dimensional properties of sub-15 nm AgNPs in the short-term (600 min) and long-term (24 weeks).

The Storage within Digital Calibration Certificates of Uncertainty Information Obtained Using a Monte Carlo Method

Supplement 1 to the ‘Guide to the expression of uncertainty of measurement’ describes a Monte Carlo method as a general numerical approach to uncertainty evaluation. Application of the approach typically delivers a large number of values of the output quantity of interest from which summary information such as an estimate of the quantity, its associated standard uncertainty, and a coverage interval for the quantity can be obtained and reported. This paper considers the use of a Monte Carlo method for uncertainty evaluation in calibration, using two examples to demonstrate how so-called ‘digital calibration certificates’ can allow the complete set of results of a Monte Carlo calculation to be reported.

Spectrometry of pulsed photon radiation

The energy distribution (spectrum) of pulsed photon radiation can hardly be measured using active devices, therefore, a thermoluminescence detector (TLD)-based few-channel spectrometer is used in combination with a Bayesian data analysis to help resolve this problem. The spectrometer consists of 30 TLD layers interspaced by absorbers made of plastics and metals with increasing atomic numbers and thickness. Thus, the main idea behind the device is the deeper the radiation penetrates—the higher the radiation’s energy when the radiation impinges perpendicular to the front of the spectrometer. From the doses measured in the TLD layers and from further prior available information, the photon spectrum is deduced using a Bayesian data analysis leading to absolute spectra and doses including their uncertainties and coverage intervals. This spectrometer was successfully used in two different scenarios, i.e. for the spectrometry of the radiation field two different industrial type open beam pulsed x-ray generators and secondly in three different radiation fields of a medical accelerator.

Sub-picoliter Traceability of Microdroplet Gravimetry and Microscopy.

Gravimetry typically lacks the resolution to measure single microdroplets, whereas microscopy is often inaccurate beyond the resolution limit. To address these issues, we advance and integrate these complementary methods, introducing simultaneous measurements of the same microdroplets, comprehensive calibrations that are independently traceable to the International System of Units (SI), and Monte-Carlo evaluations of volumetric uncertainty. We achieve sub-picoliter agreement of measurements of microdroplets in flight with volumes of approximately 70 pL, with ensemble gravimetry and optical microscopy both yielding 95% coverage intervals of ±0.6 pL, or relative uncertainties of ±0.9%, and root-mean-square deviations of mean values between the two methods of 0.2 pL or 0.3%. These uncertainties match previous gravimetry results and improve upon previous microscopy results by an order of magnitude. Gravimetry precision depends on the continuity of droplet formation, whereas microscopy accuracy requires that optical diffraction from an edge reference matches that from a microdroplet. Applying our microscopy method, we jet and image water microdroplets suspending fluorescent nanoplastics, count nanoplastic particles after deposition and evaporation, and transfer volumetric traceability to the number concentrations of single microdroplets. We expect that our methods will impact diverse fields involving dimensional metrology and volumetric analysis of microdroplets, including inkjet microfabrication, disease transmission, and industrial sprays.

On quantity, value, unit, and other terms in the JCGM International Vocabulary of Metrology

The Joint Committee for Guides in Metrology (JCGM) is in the process of revising the third edition of the International Vocabulary of Metrology (VIM3). Open discussion of the concepts, terms, and definitions used in metrology by the international community in peer reviewed journals could be constructive inputs to the JCGM deliberations on revising the VIM3. The VIM3 definitions of quantity, quantity value, unit of measurement, measurement standard, measurement, uncertainty in measurement, true quantity value, coverage interval, coverage probability, and metrology are reviewed. Some defects are observed. Proposals are offered to stimulate discussions which may lead to better concepts, terms, and definitions for metrology.

ASSESSMENT OF MEASUREMENT UNCERTAINTY FOR THE MEASUREMENT OF THE QUANTITY PERSONAL DOSE EQUIVALENT HP (10) IN PHOTON FIELDS USING THE GUM FRAMEWORK AND THE MONTE CARLO METHODS: APPLICATION TO GABON RESULTS OBTAINED DURING THE 2018 IAEA REGIONAL INTERCOMPARISON EXERCISE

This paper describes the essential of the "Guide to the expression of Uncertainty in Measurement" Framework (GUMF) Method and Monte Carlo Method (MCM) for propagating uncertainties, with an application to Gabon results obtained during the 2018 International Atomic Energy Agency (IAEA) regional intercomparison exercise. The work has shown that the output quantity Hp (10) follows a lognormal distribution. The study has also shown that although the normal distribution does not best approximate the distribution of the output quantity Hp (10), it has been observed that its estimate, the associated standard uncertainty and the coverage interval determined by GUMF and MCM were close, meaning that the application of the GUMF could still be seen as valid. Finally, the results obtained by the two methods are in agreement with International Commission on Radiological Protection and IAEA requirements for overall accuracy.

Estimating latent traits from expert surveys: an analysis of sensitivity to data-generating process

AbstractModels for converting expert-coded data to estimates of latent concepts assume different data-generating processes (DGPs). In this paper, we simulate ecologically valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data (1) recover true values and (2) construct appropriate coverage intervals. We find that the mean and both hierarchical Aldrich–McKelvey (A–M) scaling and hierarchical item-response theory (IRT) models perform similarly when expert error is low; the hierarchical latent variable models (A-M and IRT) outperform the mean when expert error is high. Hierarchical A–M and IRT models generally perform similarly, although IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable models perform poorly under most assumed DGPs.

Time-series interval prediction under uncertainty using modified double multiplicative neuron network

This paper presents a hybrid intelligent approach for constructing prediction intervals (PIs) of terrain profiles over time under uncertainty. It utilizes the double multiplicative neuron (DMN) model and the modified particle swarm optimization (MPSO) algorithm to calculate the upper and lower bounds of unknown elevations ahead on terrain profiles based on the vehicles’ track. MPSO withholds particles generating the positive PIs in the training epochs, in order to prevent the occurrence of unreasonable upside-down PIs that are brought by conventional methods. MPSO adjusts the parameters of the DMN model iteratively by minimizing the value of the proposed cost function. The fitness function aims to enhance DMN’s capability of forecasting terrain trends by integrating a trend indicator with PIs coverage probability and interval widths. This study utilizes the terrain profiles of 3 arc-seconds resolution to verify the effectiveness of the proposed MPSO-DMNT approach for one-step and multi-step PIs estimation. Experimental results demonstrate that the proposed approach (1) overcomes the limitations of the conventional PIs indicators; (2) improves the prediction accuracy for terrain trends by 18.8% in the training data and 15.4% in the testing data, and reduces the computational burden by 31.6% in the training data and 8% in the testing data over the lower upper bound estimation (LUBE) method; (3) achieves comparative coverage probability and interval widths to LUBE using a low-complexity single-layered network. The proposed hybrid approach can be used as an auxiliary decision-making tool for terrain avoidance and terrain following in flight.

Resampling-based simultaneous confidence intervals to compare scale using deviances

Resampling-based confidence intervals based on medians are examined for pairwise comparison of scale. Methods that have been found in the literature to be effective for comparing scale for two groups are extended to the case of all pairwise comparisons, using a Tukey-type adjustment to ensure simultaneous confidence. Coverage rate and interval width estimates were computed using simulated data. A permutation-based method was found to have the best coverage properties.

Confidence interval estimation for treatment effects in cluster randomization trials based on ranks.

A cluster randomization trial is one in which clusters of individuals are randomly allocated to different intervention arms. This design has become the standard for the evaluation of health care and educational strategies. To assess treatment effect, many cluster randomization trials involve outcomes that are lack meaningful units, making interpretation difficult. This difficulty may be dealt with by estimating the Mann-Whitney probability, which quantifies the probability that a typical response from one treatment arm is larger (or smaller) than a typical response from the other arm. In this work, we propose procedures for estimating this probability in cluster randomization trials. Primary emphasis is given to confidence interval estimation in trials with a small number of large clusters. The essence of the procedures is to obtain placement values based on overall ranks and arm-specific ranks prior to application of the ratio estimator, cluster-size-weighted means and mixed models for adjusting clustering effects. Nine confidence intervals were developed by applying three interval methods each based on the three variance estimators. The proposed methods can be applied to studies with binary, ordinal or continuous outcomes without making parametric assumptions. Simulation results demonstrated that the three variance estimators performed equally well, with the confidence interval procedures based on logit and inverse hyperbolic sine transformations performing better in terms of coverage and average interval width, even when the numbers of clusters are as small as 3 to 5 clusters per arm. The methods are illustrated using data from three published cluster randomization trials with SAS code provided.

High coverage of diverse invasive meningococcal serogroup B strains by the 4-component vaccine 4CMenB in Australia, 2007–2011: Concordant predictions between MATS and genetic MATS

ABSTRACT Meningococcal serogroup B (MenB) accounts for an important proportion of invasive meningococcal disease (IMD). The 4-component vaccine against MenB (4CMenB) is composed of factor H binding protein (fHbp), neisserial heparin-binding antigen (NHBA), Neisseria adhesin A (NadA), and outer membrane vesicles of the New Zealand strain with Porin 1.4. A meningococcal antigen typing system (MATS) and a fully genomic approach, genetic MATS (gMATS), were developed to predict coverage of MenB strains by 4CMenB. We characterized 520 MenB invasive disease isolates collected over a 5-year period (January 2007–December 2011) from all Australian states/territories by multilocus sequence typing and estimated strain coverage by 4CMenB. The clonal complexes most frequently identified were ST-41/44 CC/Lineage 3 (39.4%) and ST-32 CC/ET-5 CC (23.7%). The overall MATS predicted coverage was 74.6% (95% coverage interval: 61.1%–85.6%). The overall gMATS prediction was 81.0% (lower–upper limit: 75.0–86.9%), showing 91.5% accuracy compared with MATS. Overall, 23.7% and 13.1% (MATS) and 26.0% and 14.0% (gMATS) of isolates were covered by at least 2 and 3 vaccine antigens, respectively, with fHbp and NHBA contributing the most to coverage. When stratified by year of isolate collection, state/territory and age group, MATS and gMATS strain coverage predictions were consistent across all strata. The high coverage predicted by MATS and gMATS indicates that 4CMenB vaccination may have an impact on the burden of MenB-caused IMD in Australia. gMATS can be used in the future to monitor variations in 4CMenB strain coverage over time and geographical areas even for non-culture confirmed IMD cases.

Better nonparametric confidence intervals via robust bias correction for quantile regression

In this article, we revisit the problem of how to construct better nonparametric confidence intervals for the conditional quantile function from an optimization perspective. We apply the fully data‐driven bias correction procedure based on local polynomial smoothing to estimate the conditional quantile. To account for the effect of the estimated bias, we apply an asymptotic framework that the ratio of the bandwidth to the pilot bandwidth tends to some positive constant rather than zero as the sample size grows. We derive an alternative asymptotic normality of the proposed bias‐corrected quantile estimator as well as a new asymptotic variance formula. Based on theoretical results, two new pointwise confidence intervals are constructed through resampling strategies. Extensive simulation studies show that our proposed confidence intervals enjoy better performance than other competitors in terms of coverage probabilities and interval lengths and are not sensitive to the choice of bandwidth. Finally, our proposed procedure is further illustrated through United States' natality birth data in 2017.

Confidence Intervals for the Ratio of Means of Two Independent Log-Normal Distributions

In this paper, we investigate confidence intervals for the ratio of means of two independent lognormal distributions. The normal approximation (NA) approach was proposed. We compared the proposed with another approaches, the ML, GCI, and MOVER. The performance of these approaches were evaluated in terms of coverage probabilities and interval widths. The Simulation studies and results showed that the GCI and MOVER approaches performed similar in terms of the coverage probability and interval width for all sample sizes. The ML and NA approaches provided the coverage probability close to nominal level for large sample sizes. However, our proposed method provided the interval width shorter than other methods. Overall, our proposed is conceptually simple method. We recommend that our proposed approach is appropriate for large sample sizes because it is consistently performs well in terms of the coverage probability and the interval width is typically shorter than the other approaches. Finally, the proposed approaches are illustrated using a real-life example.

The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.

Coverage Intervals.

Since coverage intervals are widely used expressions of measurement uncertainty, this contribution reviews coverage intervals as defined in the Guide to the Expression of Uncertainty in Measurement (GUM), and compares them against the principal types of probabilistic intervals that are commonly used in applied statistics and in measurement science. Although formally identical to conventional confidence intervals for means, the GUM interprets coverage intervals more as if they were Bayesian credible intervals, or tolerance intervals. We focus, in particular, on a common misunderstanding about the intervals derived from the results of the Monte Carlo method of the GUM Supplement 1 (GUM-S1), and offer a novel interpretation for these intervals that we believe will foster realistic expectations about what they can deliver, and how and when they can be useful in practice.

Content Proliferation and Narrowcasting in the Age of Streaming Media

Streaming media companies have changed how contents are consumed, produced, and delivered. This research develops a theoretical model on optimal content policies for streaming media companies in order to maximize customer engagement. We have the following interesting findings. First, in contrast to the results in prior literature that firms produce just enough products without overlapping product coverage intervals, we show that overlapping coverage intervals and placing products closer can be a better policy for engagement-based firms. Second, engagement-based model produces more contents when the value of the contents is large to the customers or the profit margin in selling content is less than a certain threshold. Third, on learning a general customers’ distribution, the media firm will always overlap product coverage intervals and place programs closer when the distribution density is larger. Additionally, in facing the tradeoffs of content quality and quantity, the firm should use a higher-quality and low-variety policy for high density clusters but a lower-quality and high-variety policy for low density clusters. Furthermore, when customers consume multiple shows in a period, a better policy is to film TV shows or series with multiple episodes rather than individual movies. Our research contributes to the literature on digital media, and the results provide interesting and insightful implications for streaming companies.