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Can we elaborate a traffic-sensitive eco-driving or GLOSA (Green Light Optimal Speed Advice) strategy with a frugal amount of data when approaching an intersection? Here is the purpose of this work, which aims to adapt a traffic-theory-based estimation of the expected queue-length within mixed traffic (Connected and non-Connected Vehicles) in the vicinity of a signalized intersection. While the expected queue-length methodology was developed recently and fits natively with Eulerian traffic indicators resulting from loop sensors or cameras, this paper adapts such a methodology to Lagrangian indicators as the traces produced by any Connected Vehicle, including Floating Car or Probe Data. The main interest of the methodology lies in the frugal amount of data and expenses required to perform the traffic-sensitive speed-advisory at any connected road intersection. The full methodology is developed to extend the SPAT messages broadcast to end-users and take advantage of the Cooperative Awareness Messages (CAM) acting as GPS traces for Connected Vehicles. Contrary to Eulerian-based indicators, no supplementary and costly investment is required to collect the input data and compute the queue-length estimation. However, applying strategies based on Lagrangian indicators will affect the direct traffic observation through these indicators. Therefore, it requires to develop an assessment and predictive framework to estimate the traffic conditions. The performance of the introduced methodology is compared to alternative methods, among other Eulerian-based methods. It results from the analysis that the introduced approach performs almost as well as the ones based on exhaustive, but costly data collections. [ABSTRACT FROM AUTHOR]

Background: Japan implemented strict border control measures and all incoming passengers were subject to entry screening with reverse transcription–polymerase chain reaction or antigen testing. From late 2020, exit screening within 72 h of departure to Japan also became mandatory. In this study, we evaluated the effectiveness of the exit screening policy in Japan by analyzing airport screening data from October 2020 to April 2022. Methods: In addition to assessing entry screening data over time of passengers from the United Kingdom, we examined the prevalence of coronavirus disease 2019 (COVID-19) in the United Kingdom based on the Office of National Statistics infection survey. We constructed a statistical model that described entry screening positivity over time using Office of National Statistics prevalence data as the explanatory variable. Ideally, the time-dependent patterns of entry screening and Office of National Statistics prevalence data should resemble each other; however, we found that, sometimes, they were different and regarded the difference to statistically partly reflect the effectiveness of exit screening. Results: The average proportion positive in one month before mandatory exit screening was implemented among Japanese passengers was 0.67% (95% confidence interval [CI]: 0.45, 0.98), whereas the proportion positive decreased to 0.49% (95% CI: 0.21, 1.15) in the first month of exit screening. Adjusting for time-dependent prevalence at the origin, we concluded that exit screening contributed to reducing passenger positivity by 59.3% (95% CI: 19.6, 81.3). The overall positivity values among passengers during the Delta and Omicron variant periods were 3.46 times and 1.46 times that during the pre-Delta variant period, respectively. Conclusions: We used a simplistic statistical model and empirical data from passengers arriving in Japan from the United Kingdom to support that exit screening helped to reduce the proportion positive by 59%. Although the proportion positive later increased considerably and precluded preventing the introduction of imported cases, submitting a certificate for a negative test result contributed to reducing the positivity among travelers. [ABSTRACT FROM AUTHOR]

We give a thorough description of the asymptotic property of the maximum likelihood estimator (MLE) of the skewness parameter of a Skew Brownian Motion (SBM). Thanks to recent results on the Central Limit Theorem of the rate of convergence of estimators for the SBM, we prove a conjecture left open that the MLE has asymptotically a mixed normal distribution involving the local time with a rate of convergence of order 1/4. We also give a series expansion of the MLE and study the asymptotic behavior of the score and its derivatives, as well as their variation with the skewness parameter. In particular, we exhibit a specific behavior when the SBM is actually a Brownian motion, and quantify the explosion of the coefficients of the expansion when the skewness parameter is close to −1 or 1. [ABSTRACT FROM AUTHOR]

Background: The Japanese government has instituted border control measures against COVID-19, including entry and exit screening of people arriving from overseas. We sought to evaluate the effectiveness of the exit screening policy in Japan in reducing the risk of importing COVID-19 cases among travelers from Asian and Pacific countries. Methods: The study period was stratified based on the timing of exit screening: (i) the control period (the pre-exit screening period from 25 October 2020 to 16 January 2021), (ii) the time period with the Alpha variant from 17 January to 10 April 2021, and (iii) the time period with the Delta variant from 2 May to 2 October 2021. Incidence data in the countries of origin were used to adjust for the risk of infection among travelers. The positivity rate of entry screening in Japan was compared among the three different study periods, adjusting for the risk of infection in the country of origin. Results: The adjusted relative risk of positivity was greatly reduced and substantially below the value of 1 during the Alpha variant period compared with the control period. Although the relative risks increased when comparing the Delta variant period against control, the estimate remained below 1, except for among travelers from India and Myanmar. The relative risk reduction was greatest in high-income countries, with estimates of 100% and 96% risk reduction during the Alpha and Delta variant periods, respectively, followed by upper-middle-income countries with estimates of 90% and 76%, respectively. Conclusions: Even in the presence of the Alpha and Delta variants, exit screening clearly reduced the risk of infection among travelers arriving from Asian and Pacific nations. As the testing relies on the country of origin, the effectiveness varied greatly by the socioeconomic income status and epidemiological situation of those countries. Test standardization and quality assurance may be required in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

We consider two parsimonious models of binary high-order Markov chains and discover their ability to approximate arbitrary high-order Markov chains. Two types of global measures for approximation accuracy are introduced, theoretical and experimental results are obtained for these measures and for the considered parsimonious models. New consistent statistical parameter estimator is constructed for parsimonious model based on two-layer artificial neural network. [ABSTRACT FROM AUTHOR]

Background: A major epidemic of COVID-19 caused by the Delta variant (B.1.617.2) occurred in India from March to July 2021, resulting in 19 million documented cases. Given the limited healthcare and testing capacities, the actual number of infections is likely to have been greater than reported, and several modelling studies and excess mortality research indicate that this epidemic involved substantial morbidity and mortality. Methods: To estimate the incidence during this epidemic, we used border entry screening data in Japan to estimate the daily incidence and cumulative incidence of COVID-19 infection in India. Analysing the results of mandatory testing among non-Japanese passengers entering Japan from India, we calculated the prevalence and then backcalculated the incidence in India from February 28 to July 3, 2021. Results: The estimated number of infections ranged from 448 to 576 million people, indicating that 31.8% (95% confidence interval (CI): 26.1, 37.7) – 40.9% (95% CI: 33.5, 48.4) of the population in India had experienced COVID-19 infection from February 28 to July 3, 2021. In addition to obtaining cumulative incidence that was consistent with published estimates, we showed that the actual incidence of COVID-19 infection during the 2021 epidemic in India was approximately 30 times greater than that based on documented cases, giving a crude infection fatality risk of 0.47%. Adjusting for test-negative certificate before departure, the quality control of which was partly questionable, the cumulative incidence can potentially be up to 2.3–2.6 times greater than abovementioned estimates. Conclusions: Our estimate of approximately 32–41% cumulative infection risk from February 28 to July 3, 2021 is roughly consistent with other published estimates, and they can potentially be greater, given an exit screening before departure. The present study results suggest the potential utility of border entry screening data to backcalculate the incidence in countries with limited surveillance capacity owing to a major surge in infections. [ABSTRACT FROM AUTHOR]

This note addresses the question of optimally estimating a linear functional of an object acquired through linear observations corrupted by random noise, where optimality pertains to a worst-case setting tied to a symmetric, convex and closed model set containing the object. It complements the article 'Statistical Estimation and Optimal Recovery' published in the Annals of Statistics in 1994. There, Donoho showed (among other things) that, for Gaussian noise, linear maps provide near-optimal estimation schemes relatively to a performance measure relevant in Statistical Estimation. Here, we advocate for a different performance measure arguably more relevant in Optimal Recovery. We show that, relatively to this new measure, linear maps still provide near-optimal estimation schemes even if the noise is merely log-concave. Our arguments, which make a connection to the deterministic noise situation and bypass properties specific to the Gaussian case, offer an alternative to parts of Donoho's proof. [ABSTRACT FROM AUTHOR]

Armed conflict, displacement and food insecurity have affected Adamawa, Borno, and Yobe states of northeast Nigeria (population ≈ 12 million) since 2009. Insecurity escalated in 2013 to 2015, but the humanitarian response was delayed and the crisis’ health impact was unquantified due to incomplete death registration and limited ground access. We estimated mortality attributable to this crisis using a small-area estimation approach that circumvented these challenges. We fitted a mixed effects model to household mortality data collected as part of 70 ground surveys implemented by humanitarian actors. Model predictors, drawn from existing data, included livelihood typology, staple cereal price, vaccination geocoverage, and humanitarian actor presence. To project accurate death tolls, we reconstructed population denominators based on forced displacement. We used the model and population estimates to project mortality under observed conditions and varying assumed counterfactual conditions, had there been no crisis, with the difference providing excess mortality. Death rates were highly elevated across most ground surveys, with net negative household migration. Between April 2016 and December 2019, we projected 490,000 excess deaths (230,000 children under 5 y) in the most likely counterfactual scenario, with a range from 90,000 (best-case) to 550,000 (worst-case). Death rates were two to three times higher than counterfactual levels, double the projected national rate, and highest in 2016 to 2017. Despite limited scope (we could not study the situation before 2016 or in neighboring affected countries), our findings suggest a staggering health impact of this crisis. Further studies to document mortality in this and other crises are needed to guide decision-making and memorialize their human toll. [ABSTRACT FROM AUTHOR]

This work presents a Bayesian statistical approach to the saliency map estimation problem. More specifically, we formalize the saliency map estimation issue in the fully automatic Markovian framework. The major and original contribution of the proposed Bayesian–Markov model resides in the exploitation of a pixel pairwise modeling and a likelihood model based on a parametric mixture of two different class-conditional likelihood distributions whose parameters are adaptively and previously estimated for each image. This allows us to adapt our saliency estimation model to the specific characteristics of each image of the dataset and to provide a nearly parameter-free—hence dataset-independent—unsupervised saliency map estimation procedure. In our case, the parameters of the likelihood model are all estimated under the principles of the iterative conditional estimation framework. Once the estimation step is completed, the MPM (maximum posterior marginal) solution of the saliency map (which we show as particularly suitable for this type of estimation), is then estimated by a stochastic sampling scheme approximating the posterior distribution (whose parameters were previously estimated). This unsupervised data-driven Markovian framework overcomes the limitations of current ad hoc or supervised energy-based or Markovian models that often involve many parameters to adapt and that are finely tuned for each different benchmark database. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods and turns out to be particularly stable across a wide variety of benchmark datasets. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how close the generated data distribution is to the target distribution is central to diagnosing existing models and developing better ones. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore three approaches to statistically estimate these scores: vector quantization, non-parametric estimation, and classifier-based estimation. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of f-divergences and statistical estimation methods can quantify the gaps between the distributions of humanwritten text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We demonstrate in the vision domain that MAUVE can identify known properties of generated images on par with or better than existing metrics. In conclusion, we present practical recommendations for using MAUVE effectively with language and image modalities. [ABSTRACT FROM AUTHOR]

Whilst several methods exist to provide sample estimates of the probability distribution function at several points, for the probability density of continuous stochastic variables, only a gross representation through the histogram is typically used. It is shown that the newly introduced concept of knowable moments (K-moments) can provide smooth empirical representations of the distribution function, which in turn can yield point and interval estimates of the density function at a large number of points or even at any arbitrary point within the range of the available observations. The proposed framework is simple to apply and is illustrated with several applications for a variety of distribution functions. [ABSTRACT FROM AUTHOR]

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions. Their advantages include: 1) They can be estimated from data as statistical averages. 2) Such representations can leverage the ability of neural networks to efficiently approximate optimal solutions in function spaces. However, a systematic and practical approach to improving the tightness of such variational formulas, and accordingly accelerate statistical learning and estimation from data, is currently lacking. Here we develop such a methodology for building new, tighter variational representations of divergences. Our approach relies on improved objective functionals constructed via an auxiliary optimization problem. Furthermore, the calculation of the functional Hessian of objective functionals unveils the local curvature differences around the common optimal variational solution; this quantifies and orders the tightness gains between different variational representations. Finally, numerical simulations utilizing neural network optimization demonstrate that tighter representations can result in significantly faster learning and more accurate estimation of divergences in both synthetic and real datasets (of more than 1000 dimensions), often accelerated by nearly an order of magnitude. [ABSTRACT FROM AUTHOR]

This paper presents a modeling framework for joint estimation of a host vehicle state and a map of the road based on global navigation satellite system (GNSS) and camera measurements. We model the road using a spline representation based on lower-dimensional Bézier curves parametrized in generalized endpoints (GEPs) with implicit guarantees of continuous lane boundaries. We model the GEPs by a parameter vector having a Gaussian prior representing the uncertainty of the prior map, and provide a systematic way of defining this prior from generic map representations. Both GNSS and camera measurements, such as lane-mark measurements, have noise characteristics that vary in time. To adapt to the changing noise levels and hence improve positioning performance, we formulate the problem as a joint vehicle state, map parameter, and noise covariance estimation problem and present two noise-adaptive linear-regression Kalman filters (LRKFs); (i) an interacting multiple-model (IMM) LRKF and (ii) a variational-Bayes (VB) LRKF. We conduct a Monte-Carlo study and compare the two approaches in terms of estimation precision and computation times. Embedded implementations in an automotive-grade dSpace Micro Autobox-II indicate the real-time validity of both approaches, with turn-around times of between 2– 80 ms , depending on the problem size and if the map is updated. The results indicate that while the IMM-LRKF shows marginally better estimation accuracy, the VB-LRKF is at least a factor of 2 faster. [ABSTRACT FROM AUTHOR]

The paper proposes a new maintenance model for a repairable system. The system is inspected at deterministic times. If a failure occurs between two successive inspections, a minimal repair is performed. At inspection times, two types of maintenance actions can be performed. The first originality of the paper is to consider that the choice between these two types of maintenance depends on the fact that there has been a failure or not since the previous inspection. If no failure has occurred since the last inspection, the maintenance, simply denoted PM, corresponds to a basic preventive maintenance action, whose effect is planned by the initial maintenance plan. If at least one failure has occurred since the last inspection, the maintenance, denoted PCM, includes both the preventive action planned by the initial maintenance plan, and a specific corrective action which aims to fix the defects that have happened since the last inspection. Both types of maintenances are assumed to be imperfect, of the ARA (Arithmetic Reduction of Age) type, with different parameters. Moreover, the paper derives several statistical inference methods for this model. The usual case of complete data is first considered. The second originality of the paper is to analyse interval-censored data in this imperfect maintenance framework. This situation holds when failure times are not observed, and the only information is the fact that failures have occurred or not between two inspections. The quality of the proposed methods is assessed on simulated data. Finally, a real dataset from a gas transmission system operator is provided and analysed with the proposed approach. • A model for repairable systems with corrective maintenance and two types of imperfect preventive maintenance is proposed. • Maintenance types depend on the fact that there has been a failure or not since the previous inspection. • Statistical inference methods are developed for complete and interval-censored data. • A real dataset from a gas transmission system operator is provided and analysed with the proposed approach. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating a vector of discrete variables θ = (θ 1 , ⋯ , θ n) , based on noisy observations Y uv of the pairs (θ u , θ v) on the edges of a graph G = ([ n ] , E) . This setting comprises a broad family of statistical estimation problems, including group synchronization on graphs, community detection, and low-rank matrix estimation. A large body of theoretical work has established sharp thresholds for weak and exact recovery, and sharp characterizations of the optimal reconstruction accuracy in such models, focusing however on the special case of Erdös–Rényi-type random graphs. An important finding of this line of work is the ubiquity of an information-computation gap. Namely, for many models of interest, a large gap is found between the optimal accuracy achievable by any statistical method, and the optimal accuracy achieved by known polynomial-time algorithms. Moreover, it is expected in many situations that this gap is robust to small amounts of additional side information revealed about the θ i 's. How does the structure of the graph G affect this picture? Is the information-computation gap a general phenomenon or does it only apply to specific families of graphs? We prove that the picture is dramatically different for graph sequences converging to amenable graphs (including, for instance, d-dimensional grids). We consider a model in which an arbitrarily small fraction of the vertex labels is revealed, and show that a linear-time local algorithm can achieve reconstruction accuracy that is arbitrarily close to the information-theoretic optimum. We contrast this to the case of random graphs. Indeed, focusing on group synchronization on random regular graphs, we prove that local algorithms are unable to have non-trivial performance below the so-called Kesten–Stigum threshold, even when a small amount of side information is revealed. [ABSTRACT FROM AUTHOR]

The method of parametric estimation for hierarchical stochastic models under incomplete observations is considered. The method is based on the features of the correlation structure of hierarchical models. The main attention is paid to the practical implementation of the method. In particular, an approach is proposed that combines analytical studies and empirical verification of the solutions. Specific examples of constructing consistent estimates of the vector parameters of the distortion function are provided and illustrated by direct calculations with numerical data of the simulation model. [ABSTRACT FROM AUTHOR]

In response to the lack of information and the arguments among the decision-makers (DMs) in large-scale group decision making (LSGDM), this study proposes a method that integrates decision risk and risk attitude into LSGDM with hesitant fuzzy linguistic preference relations (HFLPRs) based on statistical estimation. Firstly, the DMs are arranged into several subgroups via their risk attitudes. Next, the whole or part of the five-number summary for subgroups from incomplete information provided by DMs are collected. After that, the optimization model and Monte Carlo simulation are utilized to obtain mean and standard deviation estimators of the decision information, and the mean estimator is regarded as the evaluation information of subgroup. Then, the hesitant fuzzy linguistic term sets (HFLTSs) with different number of linguistic variables are normalized according to their risk attitudes and confidence interval. Meanwhile, the number of members in subgroups and the decision risk level of subgroups are combined to determine the weights of subgroups. Moreover, a minimum deviation model is derived to rank alternatives. Finally, a case study, sensitivity analysis and comparison are used to guarantee the rationality and availability of the proposed method. • The DMs are classified by risk attitudes. • Optimization models are designed to achieve mean and standard deviation. • The decision risk level is used to obtain subgroup weights. • A deviation model is presented to derive the priority vector. [ABSTRACT FROM AUTHOR]

Detecting the reflection symmetry plane of an object represented by a 3D point cloud is a fundamental problem in 3D computer vision and geometry processing due to its various applications, such as compression, object detection, robotic grasping, 3D surface reconstruction, etc. Several approaches exist to solve this problem for clean 3D point clouds. However, it is a challenging problem to solve in the presence of outliers and missing parts. The existing methods try to overcome this challenge primarily by voting-based techniques but do not work efficiently. In this work, we proposed a statistical estimator-based approach for the plane of reflection symmetry that is robust to outliers and missing parts. We pose the problem of finding the optimal estimator for the reflection symmetry as an optimization problem on a 2-sphere that quickly converges to the global solution for an approximate initialization. We further adapt the heat kernel signature for symmetry invariant matching of mirror symmetric points. This approach helps us to decouple the chicken-and-egg problem of finding the optimal symmetry plane and correspondences between the reflective symmetric points. The proposed approach achieves comparable mean ground-truth error and 4.5% increment in the F-score as compared to the state-of-the-art approaches on the benchmark dataset. [ABSTRACT FROM AUTHOR]

In our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed one selects emissions from a combination of independent Markov chains evolving at the same time. Here, we propose to conduct the same study with a second scenario assuming that given the hidden process, the emission process selects emissions from a combination of independent Markov chain evolving according to their own clock. Three basic numerical examples are studied to show the proper functioning of the iterative algorithm adapted to the proposed model. [ABSTRACT FROM AUTHOR]

Background Several observational studies have shown decreases in measured influenza vaccine effectiveness (mVE) during influenza seasons. One study found decreases of 6–11%/month during the 2011–2012 to 2014–2015 seasons. These findings could indicate waning immunity but could also occur if vaccine effectiveness is stable and vaccine provides partial protection in all vaccinees ("leaky") rather than complete protection in a subset of vaccinees. Since it is unknown whether influenza vaccine is leaky, we simulated the 2011–2012 to 2014–2015 influenza seasons to estimate the potential contribution of leaky vaccine effect to the observed decline in mVE. Methods We used available data to estimate daily numbers of vaccinations and infections with A/H1N1, A/H3N2, and B viruses. We assumed that vaccine effect was leaky, calculated mVE as 1 minus the Mantel-Haenszel relative risk of vaccine on incident cases, and determined the mean mVE change per 30 days since vaccination. Because change in mVE was highly dependent on infection rates, we performed simulations using low (15%) and high (31%) total (including symptomatic and asymptomatic) seasonal infection rates. Results For the low infection rate, decreases (absolute) in mVE per 30 days after vaccination were 2% for A/H1N1 and 1% for A/H3N2and B viruses. For the high infection rate, decreases were 5% for A/H1N1, 4% for A/H3, and 3% for B viruses. Conclusions The leaky vaccine bias could account for some, but probably not all, of the observed intraseasonal decreases in mVE. These results underscore the need for strategies to deal with intraseasonal vaccine effectiveness decline. [ABSTRACT FROM AUTHOR]

We consider the problem of learning high-dimensional, nonparametric and structured (e.g., Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use k bits to communicate its sample to a central processor. We consider three different models for communication. Under the independent model, each node communicates its sample to a central processor by independently encoding it into k bits. Under the more general sequential or blackboard communication models, nodes can share information interactively but each node is restricted to write at most k bits on the final transcript. We characterize the impact of the communication constraint k on the minimax risk of estimating the underlying distribution under L2 loss. We develop minimax lower bounds that apply in a unified way to many common statistical models and reveal that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. A key ingredient in our proofs is a geometric characterization of Fisher information from quantized samples. [ABSTRACT FROM AUTHOR]

Given a high-dimensional data matrix |$\boldsymbol{A}\in{{\mathbb{R}}}^{m\times n}$|⁠ , approximate message passing (AMP) algorithms construct sequences of vectors |$\boldsymbol{u}^{t}\in{{\mathbb{R}}}^{n}$|⁠ , |${\boldsymbol v}^{t}\in{{\mathbb{R}}}^{m}$|⁠ , indexed by |$t\in \{0,1,2\dots \}$| by iteratively applying |$\boldsymbol{A}$| or |$\boldsymbol{A}^{{\textsf T}}$| and suitable nonlinear functions, which depend on the specific application. Special instances of this approach have been developed—among other applications—for compressed sensing reconstruction, robust regression, Bayesian estimation, low-rank matrix recovery, phase retrieval and community detection in graphs. For certain classes of random matrices |$\boldsymbol{A}$|⁠ , AMP admits an asymptotically exact description in the high-dimensional limit |$m,n\to \infty $|⁠ , which goes under the name of state evolution. Earlier work established state evolution for separable nonlinearities (under certain regularity conditions). Nevertheless, empirical work demonstrated several important applications that require non-separable functions. In this paper we generalize state evolution to Lipschitz continuous non-separable nonlinearities, for Gaussian matrices |$\boldsymbol{A}$|⁠. Our proof makes use of Bolthausen's conditioning technique along with several approximation arguments. In particular, we introduce a modified algorithm (called LoAMP for Long AMP), which is of independent interest. [ABSTRACT FROM AUTHOR]

Data manipulation of the LOFAR Long-Baseline Calibrator Survey (LBCS) and the Westerbork Northern Sky Survey (WENSS) catalogues are carried out in the present study. The aim of the study is to make calibrator classification statistics plots and estimations for further observations and upcoming stations. First, mean flux densities of LBCS calibrators against declination and observed station baseline length to the tied core station are plotted and the classified sources are marked. Second, we provide the designation – naming it the success rate – for the number of sources with the correlated signal against all the LBCS catalogue sources. Third, there is a trend in mean peak flux densities between stations – longer the baseline, higher mean peak flux density. Finally, estimations for upcoming and recent stations are made and some results are not encouraging. [ABSTRACT FROM AUTHOR]

In this letter, the expression of the probability density function (pdf) of the estimate of the K-factor in a reverberation chamber (RC) is discussed. [ABSTRACT FROM AUTHOR]

We consider a binary branching process structured by a stochastic trait that evolves according to a diffusion process that triggers the branching events, in the spirit of Kimmel's model of cell division with parasite infection. Based on the observation of the trait at birth of the first n generations of the process, we construct nonparametric estimator of the transition of the associated bifurcating chain and study the parametric estimation of the branching rate. In the limit n → ∞ , we obtain asymptotic efficiency in the parametric case and minimax optimality in the nonparametric case. [ABSTRACT FROM AUTHOR]

Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces LTL f. In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability for diverse process mining tasks, including discovery, checking, and drift detection. • Interestingness measures for rule-based process specifications are introduced. • A probabilistic estimation framework of single rules and entire specifications is proposed. • Measures of specifications differ from the aggregation of the measures for their individual rules. • Measures exhibit different sensitivity to process behavior changes over time. [ABSTRACT FROM AUTHOR]

A simple and efficient method to remove white Gaussian noises and physiological noises from electrocardiogram (ECG) signals is presented. It is based on simple tools usually used in digital signal processing like moving average filter, median filter, baseline drift removal and peak detection. We show by several simulations that the proposed algorithm outperforms significantly conventional median filter and moving average filter and can be considered as a valid concurrent to the standard wavelet-based method. [ABSTRACT FROM AUTHOR]

This paper proposes a new data-driven model-free Volt-Var control (VVC) scheme for the operation of radial power distribution systems. Unlike existing methods, the proposed VVC is neither reliant on model-based computer simulations nor limited to intuition/experience-driven rules for the operation of switchable capacitors and voltage regulators inside a feeder. Despite the model-free feature, the proposed scheme can still give optimal performances by using statistical estimations on measurement data such as the one provided by an advanced metering infrastructure system. Effectiveness and feasibility of the proposed idea is demonstrated by simulation studies on an IEEE 123 nodes test feeder. After few days of initial exploration, the proposed VVC is capable to give satisfactory results near to that of a model-based optimization technique. The promising outcome of this study suggests a novel application for emerging measurement and communication technologies in the operation of modern grids. [ABSTRACT FROM AUTHOR]

Highlights • Mean reversion, seasonality, memory, spike, and stochasticity are altogether captured. • Filtering algorithms harness the combined power of OU, Poisson, and HMMs. • Our approach automatically updates parameter estimates as new data arrive. • Model implementation and validation were performed using observed data. Abstract Over the last three decades, the electricity sector worldwide underwent massive deregulation. Power market participants have encountered a growing number of challenges due to competition and other pertinent factors. For most markets at present, electricity is a non-storable commodity and its price is extremely sensitive to changes in supply and demand. The evolution of electricity prices exhibits pronounced mean reversion and cyclical patterns, possesses extreme volatility and relatively frequently occurring spikes, and manifests presence of memory property. These observed features necessitate the development of models aimed to simultaneously capture such price characteristics for forecasting, risk management, and valuation of electricity-driven derivatives. Majority of current research studies examining electricity spot prices are based on one-state models. Such models though may not accurately capture the stylised behaviours of price evolution, especially during periods of sudden spikes driven by the abrupt changes of market sentiments. This paper tackles the modelling and estimation problems under a new paradigm that integrates the deterministic calendar seasons and stochastic factors governing electricity prices with a regime-switching feature. The de-seasonalised component of our proposed model has both the jump and mean-reverting properties to account for spikes and periodic cycles alternating between lower price returns and compensating periods of higher price returns. The parameters of the de-seasonalised model components are also modulated by a higher-order hidden Markov chain (HOHMC) in discrete time. This provides a mechanism to extract latent information from historical data. The HOHMC's state is interpreted as the "state of the world" resulting from the interaction of various forces impacting the electricity market. Filters are developed to generate optimal estimates of HOHMC-relevant quantities using the observation process, and these provide online estimates of model parameters. Empirical demonstrations using the daily electricity spot prices, compiled by the Alberta Electric System Operator (AESO), show that our HOHMM approach has considerable merits in terms of price data fitting and forecasting metrics. Our proposed framework driven by a hidden Markov chain with a state memory-capturing mechanism provides a powerful alternative to support the pricing of contracts in the electricity markets. [ABSTRACT FROM AUTHOR]

Semivariance is an intuitive risk measure because it concentrates on the shortfall below a target and not on total variation. To successfully use semivariance in practice, however, a statistical estimator of semivariance is needed; Josephy and Aczel provide such an estimator. Unfortunately, they have not correctly proven asymptotic unbiasedness and mean squared error consistency of their estimator since their proof contains a mistake. This paper corrects the computational mistake in Josephy-Aczel's original proof and, that way, allows researchers and practitioners in the field of downside portfolio selection, hedging, downside asset pricing, risk measurement in a regulatory context, and performance measurement to work with a meaningfully specified downside measure. [ABSTRACT FROM AUTHOR]

Model a network as an edge-weighted graph, where the (unknown) weight we of edge e indicates the frequency of observed interactions, and over time t we observe a Poisson (twe) number of interactions across edges e. How should we estimate some given statistic of the underlying network? This leads to wide-ranging and challenging problems, on which this article makes only partial progress. [ABSTRACT FROM AUTHOR]

In the context of an insurance portfolio which provides dividend income for the insurance company’s shareholders, an important problem in risk theory is how the premium income will be paid to the shareholders as dividends according to a barrier strategy until the next claim occurs whenever the surplus attains the level of ‘barrier’. In this paper, we are concerned with the estimation of optimal dividend barrier, defined as the level of the barrier that maximizes the expected discounted dividends until ruin, under the widely used compound Poisson model as the aggregate claims process. We propose a semi-parametric statistical procedure for estimation of the optimal dividend barrier, which is critically needed in applications. We first construct a consistent estimator of the objective function that is complexly related to the expected discounted dividends and then the estimated optimal dividend barrier as the minimizer of the estimated objective function. In theory, we show that the constructed estimator of the optimal dividend barrier is statistically consistent. Numerical experiments by both simulated and real data analyses demonstrate that the proposed estimators work reasonably well with an appropriate size of samples. [ABSTRACT FROM AUTHOR]

Abstract the process of modern integrated circuit (IC) design has been challenged by many factors. One of the most important challenges is the variation of device and circuit parameters during the manufacturing process. In this paper, the effects of manufacturing process variations on the gate delay have been modeled and an accurate yet low-cost simulation method for estimation of the circuit performance has been proposed. This additive method takes advantage of a 3-parameter probability density function (PDF), known as Burr distribution, to estimate the delay of each gate on the critical path. In this work, it is demonstrated that our proposed method is more accurate than previously proposed methods by taking into account the skewness of delay PDF. Although our proposed method is based on a 3-parameter PDF, we demonstrate that the simulation cost of our proposed method is no more than the conventional 2-parameter Gaussian PDF. We have compared the accuracy of our proposed method against the HSPICE simulation results. Moreover, we have compared the accuracy of our method with the most recent works with a 2-parameter PDF. The results for ISCAS85 benchmark circuits in our work have shown for 99 percentile points with average errors of 3.62, 3.49 and 2.78% in 90 nm, 45 nm and 22 nm technologies respectively. [ABSTRACT FROM AUTHOR]

Background. The seasonal incidence of influenza is often approximated as 5%-20%. Methods. We used 2 methods to estimate the seasonal incidence of symptomatic influenza in the United States. First, we made a statistical estimate extrapolated from influenza-associated hospitalization rates for 2010-2011 to 2015-2016, collected as part of national surveillance, covering approximately 9% of the United States, and including the existing mix of vaccinated and unvaccinated persons. Second, we performed a literature search and meta-analysis of published manuscripts that followed cohorts of subjects during 1996-2016 to detect laboratory-confirmed symptomatic influenza among unvaccinated persons; we adjusted this result to the US median vaccination coverage and effectiveness during 2010-2016. Results. The statistical estimate of influenza incidence among all ages ranged from 3.0%-11.3% among seasons, with median values of 8.3% (95% confidence interval [CI], 7.3%-9.7%) for all ages, 9.3% (95% CI, 8.2%-11.1%) for children <18 years, and 8.9% (95% CI, 8.2%-9.9%) for adults 18-64 years. Corresponding values for the meta-analysis were 7.1% (95% CI, 6.1%-8.1%) for all ages, 8.7% (95% CI, 6.6%-10.5%) for children, and 5.1% (95% CI, 3.6%-6.6%) for adults. Conclusions. The 2 approaches produced comparable results for children and persons of all ages. The statistical estimates are more versatile and permit estimation of season-to-season variation. During 2010-2016, the incidence of symptomatic influenza among vaccinated and unvaccinated US residents, including both medically attended and nonattended infections, was approximately 8% and varied from 3% to 11% among seasons. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the age-dependent next-generation matrix (NGM) for the 2009 H1N1 influenza pandemic and forecast the age-stratified cumulative incidence in Japan.Methods: Using a renewal equation model that describes the time evolution of the 2009 H1N1 influenza pandemic, we derive the likelihood function to estimate parameters of the NGM and reporting coverage. Comparing the Akaike Information Criterion of models using empirically observed data from the 2009 pandemic in Gifu, Japan, we excluded redundant parameters and identified the three best models that were parameterized in different ways.Results: The initial proportions of susceptible populations were suggested as redundant information to be inferred. The three models selected successfully captured the order of the age-dependent cumulative incidence. We found that the time required for reliable estimation of age-dependent cumulative incidence was at least 180 days.Conclusions: To forecast the age-dependent cumulative incidence reliably following the estimation of the NGM and reporting coverage, we need empirically observed data for more than 5 months from the start of the epidemic, which is likely to be after the peak. To increase the practical efficacy in forecasting the cumulative incidence, additional data and approaches are required. [ABSTRACT FROM AUTHOR]

In the field of computer virus, a growing number of mathematical models have been published during the last decade. In this paper, we consider several estimation methods under two parametric models, the random constant scanning model and the nonhomogeneous random scanning model, to propose a comparison of the estimators’ performances based on the simulation of random populations. We conclude our work with an application of the different estimation methods to a real data set of worm propagation. [ABSTRACT FROM AUTHOR]

A dithering halftone is typically binary and generated by comparing the continuous-tone levels with periodic thresholds, which are two-dimensional repetition of a basic threshold matrix called the dither matrix. In many applications it is required to estimate the thresholds (that is, the period and the dither matrix) for a halftone. The period can be estimated conventionally by examining the autocorrelation functions or Fourier spectra. This paper focuses on the estimation of the dither matrix. In this paper, we regard the dithering at every site as a testing of getting a black halftone pixel and then estimate the dither matrix based on the pixel histograms of the halftone. Such a pixel-histogram based approach (PHA) is in contrast to the conventional full-dot-pattern based approach (FDPA), which is based on block histograms. The use of pixel histograms provides better efficiency: the usage of more information (on the whole rather than partial image data), the stronger resistance to the damage caused by the size increasing of dither, and the simpler computation. The experimental results show that compared with the FDPA, our PHA has higher percentages of successful estimates, is less damaged by the increase of dither size, and is computationally simpler. [ABSTRACT FROM AUTHOR]

To obtain optimal performance, estimation of radio refractivity is essential in planning and design of radio links/systems. The dependence of radio refractivity on different climatic parameters such as temperature, pressure, humidity and scale height is studied. The method proposed by International Telecommunication Union (ITU) is used in calculation of tropospheric radio refractivity. The main objective of this work is to formulate a mathematical equation/expression which can be used to predict/estimate the value of radio refractivity for an arbitrary day of any year, with a low error of estimation, in a local environment. Sensitivity of radio refractivity to temperature, pressure and humidity has been evaluated for a period of 5 years from 2008 to 2012, in a particular area under consideration. The results presented for tropospheric radio refractivity take into consideration both the location height and scale height parameters. The results are analysed in terms of statistical measures such as the moving averages, probability density function, monthly mean values, and their corresponding standard deviations. Finally, a mathematical model is formulated to calculate the radio refractivity for any day of a whole year. Reliability of error analysis in respect to accuracy is also shown. It was implemented in the industrial enterprise. [ABSTRACT FROM AUTHOR]

One of the most used variants of hidden Markov models (HMMs) is the standard casewhere the time is discrete and the state spaces (hidden and observed spaces) are finite. In this framework, we are interested in HMMs whose emission process results from a combination of independent Markov chains. Principally, we assume that the emission process evolves as follows: given a hidden state realization k at time t, an emission is a realization of a Markov chain Yk't at time t, and for two different hidden states k and k', Yk't and Yk'tt are assumed independent. Given the hidden process, the considered emission process selects its realizations from independent and homogeneous Markov chains evolving simultaneously. In this paper, we propose to study the three known basic problems of such anHMMvariant, by deriving corresponding formulas and algorithms. This allows us to enrich the set of application scenarios of HMMs. Numerical examples are presented to show the applicability of our proposed approach by deriving statistical estimations. [ABSTRACT FROM AUTHOR]

In this study, a series of electro-osmosis experiments were conducted using Dalian marine soil to evaluate the water content and shear strength of soil samples after electro-osmosis experimental treatment. The conditions that were considered in these experiments include voltage, preloading pressure, drainage direction, temperature, anode material, and processing duration. In comparing water content and shear strength, the different operational conditions were demonstrated to affect the results of electro-osmosis to a variable extent. For example, the shear strength of samples at 35°C improved by an average of 61.96% compared with room temperature samples. Also, water content under 12 V trials decreased by an average of 9.30% compared with 6 V. These results can be attributed to the difference in electro-dewatering capacities of the conditions mentioned above. Based on the data, two statistical relationships, which represent the relationship between experimental conditions and results, were also established. [ABSTRACT FROM PUBLISHER]

In our days in the social sciences, time series (or longitudinal data) are ubiquitous, used in any analytic process, with the main scope to estimate or predict the future. The main issues are represented by the large variety of time series (sometime with an unknown size), the identification of outliers, and by the impossibility to estimate the error or numerical stability of statistical analysis. This paper proposed a matrix-based model for predictive analytics and, using a statistical estimation for different finite samples extracted from time series, estimated the residual and factorial variance for a group of samples. The proposed methods are applied on different samples of social data: number of births in a community, number of inhabitants, natural mobility of population, life expectancy (by sex and area), life expectancy at birth, fertility rate, infant mortality rate. [ABSTRACT FROM AUTHOR]

Expectiles were introduced by Newey and Powell in 1987 in the context of linear regression models. Recently, Bellini et al. revealed that expectiles can also be seen as reasonable law-invariant risk measures. In this article, we show that the corresponding statistical functionals are continuous w.r.t. the 1-weak topology and suitably functionally differentiable. By means of these regularity results, we can derive several properties such as consistency, asymptotic normality, bootstrap consistency and qualitative robustness of the corresponding estimators in nonparametric and parametric statistical models. [ABSTRACT FROM AUTHOR]

Using multiple stochastic integrals and the Malliavin calculus, we analyze the asymptotic behavior of the adjusted quadratic variation for a sub-fractional Brownian motion. We apply our results to construct strongly consistent statistical estimators for the self-similarity of sub-fractional Brownian motion. [ABSTRACT FROM PUBLISHER]

Based on the statistical property of the phase and the spatial frequency relationship between the object and reference waves, a simple estimation method for phase shift extraction is proposed for generalized two-step phase-shifting interferometry. In this method, a phase shift constrained within 0–πis firstly worked out with statistics, and then it is corrected as its principal value within the range (0, 2π) except the singular value ofπ, according to the sign of tangent form of one half of the relative phase shift. Thus, this method provides a possible method to solve the uncertainty of the phase shift existing in some common algorithms. After the determination of the phase shift, a phase can be retrieved easily. The feasibility and the accuracy of the method were verified by numerical simulations and an optical experiment. In addition, this method is applicable to two or more frames off-axis generalized PSI, including slightly off-axis situation. [ABSTRACT FROM AUTHOR]

Models used in simulation may give accurate shortterm trajectories but distort long-term (statistical) properties. In this work, we augment a given approximate model with a control law (a 'thermostat') that gently perturbs the dynamical system to target a thermodynamic state consistent with a set of prescribed (possibly evolving) observations. As proof of concept, we provide an example involving a point vortex fluid model on the sphere, for which we show convergence of equilibrium quantities (in the stationary case) and the ability of the thermostat to dynamically track a transient state. [ABSTRACT FROM AUTHOR]

Reference governors (RGs) provide an effective method for ensuring safety via constraint enforcement in closed-loop nonlinear control systems. When the system parameters are uncertain but constant, robust formulations of RGs that consider only the worst-case effect may be overly conservative and exhibit poor performance. This paper proposes a parameter-adaptive reference governor (PARG) architecture that is capable of generating safe trajectories in spite of parameter uncertainties, without being as conservative as robust RGs. The proposed approach employs machine learning on a combination of off-line simulations and on-line measurements to estimate parameter-robust constraint-admissible sets (PRCASs) that can be leveraged by the PARG. We illustrate the robust set learning and constraint enforcement qualities of the PARG using a two-dimensional electromagnetic actuator example, and further demonstrate the potential of the PARG on a vehicle case study for preventing rollover despite aggressive maneuvering. [ABSTRACT FROM AUTHOR]

In this paper, we review some results for insurance risk theory. We first introduce a variety of the insurance risk models proposed thus far. Then, we show that the expected discounted penalty function (the so-called Gerber-Shiu function) can describe some risk indicators. Next, the dividend problem is discussed; more precisely, the (approximated) optimal dividend barrier is derived and other extended dividend strategies introduced. In addition, some modified models depending on reinsurance or tax are introduced. Finally, we discuss the statistical estimation of the ruin probability and the Gerber-Shiu function. [ABSTRACT FROM AUTHOR]

The iTrust system is a decentralized and distributed publication, search and retrieval system that makes it difficult to censor or filter information accessed over the Internet. iTrust employs a completely distributed membership algorithm, and a detection and defensive adaptation algorithm that protects against malicious nodes in the membership. These algorithms depend on information that cannot be observed or measured directly, the current size of the membership and the current proportion of malicious nodes in the membership. They use statistical estimation to approximate these network environment parameters, and then they dynamically adapt accordingly. These algorithms provide accurate and timely management of the iTrust system in the presence of membership churn and malicious nodes. [ABSTRACT FROM AUTHOR]

Denoising has to do with estimating a signal $$\mathbf {x}_0$$ from its noisy observations $$\mathbf {y}=\mathbf {x}_0+\mathbf {z}$$ . In this paper, we focus on the 'structured denoising problem,' where the signal $$\mathbf {x}_0$$ possesses a certain structure and $$\mathbf {z}$$ has independent normally distributed entries with mean zero and variance $$\sigma ^2$$ . We employ a structure-inducing convex function $$f(\cdot )$$ and solve $$\min _\mathbf {x}\{\frac{1}{2}\Vert \mathbf {y}-\mathbf {x}\Vert _2^2+\sigma {\lambda }f(\mathbf {x})\}$$ to estimate $$\mathbf {x}_0$$ , for some $$\lambda >0$$ . Common choices for $$f(\cdot )$$ include the $$\ell _1$$ norm for sparse vectors, the $$\ell _1-\ell _2$$ norm for block-sparse signals and the nuclear norm for low-rank matrices. The metric we use to evaluate the performance of an estimate $$\mathbf {x}^*$$ is the normalized mean-squared error $$\text {NMSE}(\sigma )=\frac{{\mathbb {E}}\Vert \mathbf {x}^*-\mathbf {x}_0\Vert _2^2}{\sigma ^2}$$ . We show that NMSE is maximized as $$\sigma \rightarrow 0$$ and we find the exact worst-case NMSE, which has a simple geometric interpretation: the mean-squared distance of a standard normal vector to the $${\lambda }$$ -scaled subdifferential $${\lambda }\partial f(\mathbf {x}_0)$$ . When $${\lambda }$$ is optimally tuned to minimize the worst-case NMSE, our results can be related to the constrained denoising problem $$\min _{f(\mathbf {x})\le f(\mathbf {x}_0)}\{\Vert \mathbf {y}-\mathbf {x}\Vert _2\}$$ . The paper also connects these results to the generalized LASSO problem, in which one solves $$\min _{f(\mathbf {x})\le f(\mathbf {x}_0)}\{\Vert \mathbf {y}-{\mathbf {A}}\mathbf {x}\Vert _2\}$$ to estimate $$\mathbf {x}_0$$ from noisy linear observations $$\mathbf {y}={\mathbf {A}}\mathbf {x}_0+\mathbf {z}$$ . We show that certain properties of the LASSO problem are closely related to the denoising problem. In particular, we characterize the normalized LASSO cost and show that it exhibits a 'phase transition' as a function of number of observations. We also provide an order-optimal bound for the LASSO error in terms of the mean-squared distance. Our results are significant in two ways. First, we find a simple formula for the performance of a general convex estimator. Secondly, we establish a connection between the denoising and linear inverse problems. [ABSTRACT FROM AUTHOR]

In this paper, we review three-dimensional (3D) photon counting imaging with axially distributed sensing. Under severely photon-starved conditions, we have proposed various imaging and algorithmic approaches to reconstruct a scene in 3D, which are not possible by using conventional imaging system due to lack of sufficient number of photons. In this paper, we present an overview of optical sensing and imaging system along with dedicated algorithms for reconstructing 3D scenes by photon counting axially distributed sensing, which may be implemented by moving a single image sensor along its optical axis. To visualize the 3D image, statistical estimation methods and computational reconstruction of axially distributed sensing is applied. [ABSTRACT FROM AUTHOR]