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This paper investigates the practicality of an autonomous and resilient positional navigation using corner cube reflectors on the lunar surface. The proposed approach stands as an efficient navigation solution when the current system, e.g. GPS, is unavailable due to being attacked, jammed or spoofed by a third party. The paper provides a complete setup of the mathematical models using a nonlinear least squares algorithm and covariance analysis. A study is done that determines how the blockage of the Earth affects the proposed solution’s ability to perform positional navigation. Numerical simulations are presented to show the accuracy, performance and practicality of the proposed solution. The results of the proposed solution are compared with the results of an Earth-based ground station system that uses radio frequency to track the given satellite. The results show that the lunar reflector-based system generates more accurate results than the Earth-based ground station system. [ABSTRACT FROM AUTHOR]

Building and other construction assets constitute a significant part of a country's physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries' specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980-2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country's construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development. [ABSTRACT FROM AUTHOR]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]

This paper analyses the development of private actions for damages as a significant pillar of private enforcement of EU competition law and discusses the quantification and estimation of harm. Since the adoption of the EU Directive 2014/104/EU on Antitrust Damages Actions, private enforcement in Europe has undergone crucial clarifications in the case law of EU courts, yet the critical issue of quantifying damages in private actions has only recently been addressed by the Court of Justice of the European Union (CJEU). This paper takes a closer look at the recent ruling in the Tráficos Manuel Ferrer case (C-312/21) and assesses the implications of that case law for private enforcement proceedings. Concerning the allocation of procedural costs, this judgment has established that injured parties may be required to bear a cost risk, even if they are partially successful. This finding needs further reflection in the context of consumer claims for damages, as it is susceptible to add an additional cost burden on plaintiffs. The second intricate finding concerns the relation between the ability of national courts to estimate damages and the exhaustion of evidence disclosure. Taking the example of Spain, the paper advocates for the specific inclusion of the principle of proportionality when determining harm in private actions for damages and especially in cases involving consumers or smaller harmed parties. [ABSTRACT FROM AUTHOR]

A recent paper introduced the interpolating family (IF) of distributions, and they also derived various mathematical properties of the family. Some of the most important properties discussed were the integer order moments of the IF distributions. The moments were expressed as an integral (which were not evaluated) or as finite sums of the beta function. In this paper, more general expressions for moments of any integer order or any real order are derived. Apart from being more general, our expressions converge for a wider range of parameter values. The expressions for entropies are also derived, the maximum likelihood estimation is considered and the finite sample performance of maximum likelihood estimates is investigated. [ABSTRACT FROM AUTHOR]

In this paper, we study the evasion game of high speed evader involving two pursuers and a single evader with geometric constraints on the control parameters of the players in the plane. The game is described by linear equations. Evasion is said to be possible if the state of the evader doesn’t coincide with the state of any pursuer for all time. We construct an evasion strategy for the evader which ensure completion the evasion game for any initial positions of players. In addition, we introduce a new concept of approach times and demonstrate that the number of approach times does not exceed 3. [ABSTRACT FROM AUTHOR]

Due to the emergence of social networking platforms, a large number of users around the world are being part and partial of this platform. At a fraction of the time users on social media are communicating digital files in the form of text, video, images, voice and music which ultimately generates big data. The matter of interest is to estimate precisely the average file size at time duration (occasion). The time may hours or days or months. This paper presents a sample-based methodology to deal with mean size estimation of digital communication content spreading on a social media platform. An estimator is suggested using a random sample from big data and its properties are derived. A simulation method is suggested that computes the confidence interval (CI) for the prediction of précised range of digital file size. The proposed method produces an optimal confidence interval at the suitable choice of constant. These estimated confidence intervals can be used for developing α-control charts for constant monitoring of the growth in file size in social media storage at the data centre. If the growth of mean digital file size crosses the upper limit then additional storage infrastructure is needed at the administration level of the social media site. One can generate machine learning algorithms proposed method for monitoring the growth of average digital file size over time duration. [ABSTRACT FROM AUTHOR]

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed. [ABSTRACT FROM AUTHOR]

Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a new probability model called as Maxwell-Boltzmann-Exponential (MBE) distribution. The MBE distribution arises as a mixture distribution of the Maxwell-Boltzmann and exponential distributions. The statistical properties of the distributions are studied and obtained in closed-form expressions. Three methodologies are assessed and compared for the estimation of parameters in the MBE distribution. The MBE regression model is defined, with the proposed regression model being an alternative to the gamma regression model for response variables that are extremely right-skewed and bimodal. Two real data sets are used to demonstrate the applicability of the proposed models against the existing models. [ABSTRACT FROM AUTHOR]

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets. [ABSTRACT FROM AUTHOR]

When data exhibit a high frequency of small to medium values and a low frequency of large values, fitting a classical distribution might fail. This is why spliced models defined from different distributions on distinct intervals are proposed in the literature. In contrast to the intensive study of two-spliced distributions, the case with more than two components is scarcely approached. In this paper, we focus on three-spliced distributions and on their ability to improve the modeling of extreme data. For this purpose, we consider a popular insurance data set related to Danish fire losses, to which we fit several three-spliced distributions; moreover, the results are compared to the best-fitted two-spliced distributions from previous studies. [ABSTRACT FROM AUTHOR]

In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles. [ABSTRACT FROM AUTHOR]

The use of life distributions has increased over the past decade, receiving particular attention in recent years, both from a practical and theoretical point of view. Life distributions can be used in a number of applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. This paper presents and investigates a new life distribution. The proposed model shows favorable characteristics in terms of reliability theory, which makes it competitive against other commonly used life distributions, such as the exponential, gamma, and Weibull distributions. The methods of maximum likelihood and moments are used to estimate the parameters of the proposed model. Additionally, real-life data drawn from different fields are used to illustrate the usefulness of the new distribution. Further, the R programming language is used to perform all computations and produce all graphs. [ABSTRACT FROM AUTHOR]

In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools. [ABSTRACT FROM AUTHOR]

While the evolution of state uncertainties from Gaussian to non-Gaussian is well understood in geocentric orbits, the only data product made publicly available by the United States Space Force, the Two-Line Element, does not include state uncertainties at all. With a new international focus on cislunar space situational awareness, it is useful to determine how this complex dynamical environment influences trajectory uncertainties and the resultant implications for data association, orbit determination, and force model algorithms. This paper utilizes a new tensor eigenpair measure of nonlinearity (TEMoN) to quantify the nonlinearity of gravitational forces in the cislunar regime. This measure is then compared with various characterizations of Gaussian distributions to determine the value of TEMoN at which trajectory uncertainties become non-Gaussian. This novel advancement combines the cumulative effect of time and physical location of the trajectory into one value. The result is a predictive method that obviates highly parameterized Monte Carlo runs, and allows an objective assessment of how sensor tasking cadence, measurement uncertainty, and force model selection can be balanced to enable nascent cislunar space situational awareness capabilities with legacy space surveillance network assets. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel statistical framework based on the first two quantile conditional moments that facilitates effective goodness-of-fit testing for one-sided Lévy distributions. The scale-ratio framework introduced in this paper extends our previous results in which we have shown how to extract unique distribution features using conditional variance ratio for the generic class of α-stable distributions. We show that the conditional moment-based goodness-of-fit statistics are a good alternative to other methods introduced in the literature tailored to the one-sided Lévy distributions. The usefulness of our approach is verified using an empirical test power study. For completeness, we also derive the asymptotic distributions of the test statistics and show how to apply our framework to real data. [ABSTRACT FROM AUTHOR]

The growth in energy demand, particularly in the oil & gas sector, influences investment decisions for jetty development to enhance supply capacity. The Dolphin Jetty, a prevalent type used by the National Oil Company, incorporates cylindrical or pile-like structures called dolphins to guide and moor vessels safely. As energy consumption in Indonesia shows a consistent upward trend, optimizing the supply chain and fuel distribution becomes imperative. In this context, maritime transportation, specifically through dolphin jetties, emerges as a strategic choice to efficiently meet fuel distribution needs. This paper emphasizes the importance of preparing Capital Expenditure (CAPEX) documentation in the feasibility studies stage, influencing the economic value of the investment. The paper uses historical data to introduce a parametric modeling approach for Dolphin-configured jetties, considering variations in mooring capacity, dead weight tonnage (DWT), and estimated construction implementation years. The outcomes aim to inform the CAPEX Class 4 preparation, contributing valuable insights for the economic assessment of jetty construction projects. [ABSTRACT FROM AUTHOR]

This paper presents a trajectory control system design for a quadcopter, an unmanned aerial vehicle (UAV), which is based on estimated parameters that are assumed to exhibit random walk behavior. Initially, the rotational dynamic model of the UAV is formulated using the Newton Euler method in terms of angular velocity about the x, y, and z axes. This model is then simplified into three separated-first-order linear differential equations, with coefficients derived from the combined effects of inertia, aerodynamic drag, and gyroscopic effects, referred to as lumped parameters. A Proportional-Integral (PI) controller with feed-forward design is then developed to control this simplified model. To adapt the controller to the lumped parameters that exhibit random walk behavior, each simplified equation is restructured into a processing and measurement model. The states of these models are estimated by using the Unscented Kalman Filter (UKF). These estimated values are then utilized to adjust the PI gains and compensate the signal of the designed angular velocity controller, transforming it into an adaptive controller. The entire UAV controller comprises two main parts, an inner loop for adaptive angular rate control and an outer loop serving as an attitude-thrust controller. The proposed controller is simulated using Simulink, with circular and square trajectories. The simulation results demonstrate that the quadcopter successfully follows the desired circular and square paths. The steady-state error for the x and y axes in the square trajectory is less than 0.05 meters within 5 seconds, and for the z axis, it is less than 0.02 meters within 2.5 seconds. The controller gains do not require adjustment when changing trajectories. Moreover, the estimated parameters remain nearly constant at steady state. [ABSTRACT FROM AUTHOR]

The class of α -stable distributions is widely used in various applications, especially for modeling heavy-tailed data. Although the α -stable distributions have been used in practice for many years, new methods for identification, testing, and estimation are still being refined and new approaches are being proposed. The constant development of new statistical methods is related to the low efficiency of existing algorithms, especially when the underlying sample is small or the distribution is close to Gaussian. In this paper, we propose a new estimation algorithm for the stability index, for samples from the symmetric α -stable distribution. The proposed approach is based on a quantile conditional variance ratio. We study the statistical properties of the proposed estimation procedure and show empirically that our methodology often outperforms other commonly used estimation algorithms. Moreover, we show that our statistic extracts unique sample characteristics that can be combined with other methods to refine existing methodologies via ensemble methods. Although our focus is set on the symmetric α -stable case, we demonstrate that the considered statistic is insensitive to the skewness parameter change, so our method could be also used in a more generic framework. For completeness, we also show how to apply our method to real data linked to financial market and plasma physics. [ABSTRACT FROM AUTHOR]

An engine with a cylinder dedicated to providing enriched EGR (D-EGR) has been demonstrated by Southwest Research Institute (SwRI). A vehicle equipped with the D-EGR engine showed more than 10% of fuel economy improvement over various driving cycles. Despite a much higher level of EGR than a conventional engine, combustion in the engine is maintained stable since reformates such as H2 and CO from rich combustion increase dilution tolerance. Thus, it is deemed that precise real-time information of the level of reformates in the engine is critical to maintain stable combustion especially during transients. In this paper, a method to estimate intake burned gas and reformate mass fractions of the D-EGR engine is developed and validated through simulations. The estimated fractions can be used to control spark timing in real-time to prevent knocking or misfire during transients in the D-EGR engine. [ABSTRACT FROM AUTHOR]

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]

Wave energy generation methods have significant energy costs. The implementation of sophisticated control techniques in wave energy generators can lower the cost of power generation by optimizing the energy recovered from wave energy converters (WECs). To determine control inputs, most control systems rely on knowledge of the wave excitation force, including information on past, present, and future excitation forces. For the excitation of WEC devices, wave excitation force can only be inferred and predicted because it is an unmeasurable quantity. One of the more widely used observers in wave excitation estimates at the moment is the Kalman filter, but its use is primarily restricted to linear Kalman filtering. The mooring system is an integral component of floating wave energy producers. The mooring force of the device is actually nonlinear; however, the majority of current studies on excitation estimates for wave energy producers based on Kalman filter methods employ an ideal motion model based on the linearization of the mooring force. This paper, in an attempt to make things more realistic, creates a WEC system with highly nonlinear mooring forces, suggests a way to build a wave excitation force estimator for a nonlinear WEC system using the extended Kalman filtering method, and assesses the impact of various factors, such as measurement noise, random phase, and the number of equal-energy methods dividing the frequency, on the accuracy of the wave excitation force estimate. [ABSTRACT FROM AUTHOR]

Summary: The paper identifies a Wiener system, which is excited by a cyclostationary time series. To estimate the first subsystem's linear dynamic impulse response: this proposed algorithm first kernel‐windows the Wiener system's input measurements, then cross‐correlates with the output time series. To identify the second subsystem's static nonlinearity: this proposed algorithm first estimates the unobservable inter‐block internal signal (consistently in the statistical sense), and then kernel‐windows these estimates with the Wiener system output. This estimator provides the unusual capability to identify non‐invertible nonlinearities. This strategy removes any restrictive requirement for a Gaussian random excitation or a sinusoidal deterministic excitation. This paper further proves the estimator's asymptotic consistency and determines the kernel bandwidth for algorithmic convergence. The proposed algorithm's efficacy is verified in the context of two common applications: a servo mechanical system and a telecommunication channel. [ABSTRACT FROM AUTHOR]

INAR models have the great advantage of being able to capture the conditional distribution of a count time series based on their past observations, thus allowing it to be tailored to meet the unique characteristics of count data. This paper reviews the two-parameter Poisson extended exponential (PEE) distribution and its corresponding INAR(1) process. Then the INAR of order p (INAR(p)) model that incorporates PEE innovations is proposed, its statistical properties are presented, and its parameters are estimated using conditional least squares and conditional maximum likelihood estimation methods. Two practical data sets are analyzed and compared with competing INAR models in an effort to gauge the performance of the proposed model. It is found that the proposed model performs better than the competitors. [ABSTRACT FROM AUTHOR]

The paper presents an improvement of the n-shifting technique to identify the frequency response of an industrial process using a fully asymmetric and delaying relay. The n-shifting approach allows the calculation of n + 1 points of G(s) by an asymmetric relay experiment. This set of n points is composed of G(0), G(jωosc), ... , G(jnωosc), being ωosc the oscillation frequency, and where G(jωosc) is in most cases located in the third quadrant of the Nyquist map. By delaying the relay output and repeating a similar experiment it can be generated n additional points of G(s) where the first point is G(jω'osc) with 0 < ω'osc < ωosc. In this way, it is possible to depict the full output spectrum of G(s) from zero to very high frequencies by a short relay experiment. An example of identification and tuning of a PID controller with data from the n-shifting are presented to show the validity of the approach. [ABSTRACT FROM AUTHOR]

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately. [ABSTRACT FROM AUTHOR]

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

In this paper, adaptive controllers are designed to track a given trajectory for linear and nonlinear systems. No condition on the tracked trajectory, other than continuity and boundedness, is needed to simultaneously ensure exponential convergence to the tracking reference, exponential convergence to the identification of the plant, and robustness to nonparametric uncertainties. To achieve this, the formulation of the excitation condition associated with the identification part of the adaptive scheme is proposed without employing closed-loop signals, allowing the use of a transient enrichment of the reference. The effect of this transient modification is attenuated by using relaxed requirements for the identification, obtained through a generalization of several estimation algorithms found in recent literature that use memory mechanisms. Consequently, no spectral content of the tracked trajectory--a classic requirement in adaptive theory--is needed to guarantee the mentioned features when the proposed scheme is used. A numerical example is given to illustrate the design aspects involved and the distinctive features of the proposed strategy. [ABSTRACT FROM AUTHOR]

The probability of ruin of an insurance company is one of the main risk measures considered in risk theory, and the problems of its calculation and approximation have attracted much attention. Statistical estimations have been developed on the ruin probability in infinite time for insurance loses from heavy-tailed distributions. However, these estimation suffer heavily from under-coverage or have a robustness problem. We therefore need another method for estimating the probability of ruin in infinite time for heavy-tailed losses. In this paper, we introduce a robust estimator of the infinite-time probability of ruin for such distributions. Our methodology is based on extreme value theory, which offers adequate statistical results for such distributions. Our approach is based on a sensitive distribution known as the t-Hill estimator (t-score or score moment estimation) for the index of any tail distribution and introduced in Fabiáan and Stehlík. We establish their asymptotic normality, and through a simulation study, illustrate their behaviour in terms of absolute bias and mean squared error. The simulation results show that our estimators perform well and that they are fairly robust to outliers. [ABSTRACT FROM AUTHOR]

There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper. [ABSTRACT FROM AUTHOR]

Metric regularity is widely concerned since its important applications in optimization and control theory. For promoting the application of metric regularity, it is valuable to study the estimation of the neighborhood which makes the regularity hold. However, it seems that no result has been established about this issue. This paper investigates the estimation of the neighborhood of metric regularity for quadratic functions. The main result gives the expression of the neighborhood of metric regularity for a kind of convex quadratic functions. [ABSTRACT FROM AUTHOR]

Modern science is dependent on imaging on the nanoscale, often achieved through processes that detect secondary electrons created by a highly focused incident charged particle beam. Multiple types of measurement noise limit the ultimate trade-off between the image quality and the incident particle dose, which can preclude useful imaging of dose-sensitive samples. Existing methods to improve image quality do not fundamentally mitigate the noise sources. Furthermore, barriers to assigning a physically meaningful scale make the images qualitative. Here, we introduce ion count-aided microscopy (ICAM), which is a quantitative imaging technique that uses statistically principled estimation of the secondary electron yield. With a readily implemented change in data collection, ICAM substantially reduces source shot noise. In helium ion microscopy, we demonstrate 3x dose reduction and a good match between these empirical results and theoretical performance predictions. ICAM facilitates imaging of fragile samples and may make imaging with heavier particles more attractive. [ABSTRACT FROM AUTHOR]

In recent years, the modeling of time-to-events has emerged as a highly promising and dynamic research area. This field has witnessed a surge of research studies dedicated to developing novel statistical methodologies aimed at effectively handling time-to-event phenomena. These studies are motivated by the increasing recognition of the importance of time-related factors in various fields such as medicine, epidemiology, finance, and engineering. Researchers have been actively engaged in proposing innovative approaches to address the complexities associated with time-to-event data. The overarching goal is to enhance our understanding of event occurrence and duration, enabling more accurate predictions and informed decision-making. This research encompasses a wide range of topics, including survival analysis, reliability modeling, and event prediction. The motivation behind these research efforts stems from the need to overcome traditional limitations in time-to-event analysis and to explore new avenues for modeling and interpretation. By introducing advanced statistical techniques, researchers seek to capture the intricate dynamics of event processes, considering factors such as censoring, competing risks, and time-varying covariates. The proliferation of research studies in this domain reflects a collective effort to push the boundaries of statistical modeling and analysis, paving the way for more comprehensive and robust methodologies. As researchers continue to delve deeper into the intricacies of time-to-event data, the impact of these advancements extends to diverse applications, ultimately fostering innovation and progress across interdisciplinary fields. This paper adopts and implements a new statistical approach to propose a family of flexible distributions, namely, a new generalized- O family of distributions. For the newly obtained family, certain mathematical properties such as identifiability, quantile function, r th non-central moment, Lorenz curve, incomplete moments, and the expression of the Bonferroni curve are obtained. Furthermore, an extension of the Weibull model is introduced using the newly developed approach, namely, a new generalized Weibull model. The parameters of the new generalized version of the Weibull model are estimated by adopting a well-known estimation approach. Finally, a data set consists of sixty (60) observations representing the times of the survival of some patients infected by the COVID-19 epidemic is analyzed to illustrate the new generalized Weibull model. [ABSTRACT FROM AUTHOR]

Can inefficiency be rational? Excess resources or slack may serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. Slack may however also be the result of inefficiency. In Bogetoft and Kerstens, Distinguishing useful and wasteful slack, we propose an approach to separate useful and wasteful slack. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. The literature on organization and strategic management suggests that slack in the form of excess resources may be useful. It may, for example, serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. In contrast, the economic literature tends to view slack as wasteful. When the same products and services can be produced with fewer resources and slack per se is not assigned any value, slack should be eliminated. The aim of this paper is to reconcile these two perspectives. We acknowledge that slack may be both useful and wasteful. The challenge is how to separate the two. Our approach relies on the simple Pareto idea. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. Funding: Financial support from Det Frie Forskningsråd [Grant 9038-00042A] is greatly appreciated. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2415. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates a novel category of probability distributions and a specific member within this category. We have formulated a new family of trigonometric distributions by utilizing the odds ratio derived from the distribution function of a base distribution. This newly devised distribution family termed the “Sine pie-power odd-G family” of distributions, is constructed through a transformation involving the sine function. The paper presents an overview of the fundamental characteristics inherent to this proposed distribution family. Using the Weibull distribution as a base reference, we have introduced a member belonging to the proposed distribution family. This member demonstrates various hazard functions such as j, reverse-j, increasing, decreasing, or bathtub shapes. The paper examines essential statistical attributes of this newly introduced distribution. The estimation of the distribution’s parameters is carried out via the maximum likelihood estimation method. The accuracy of the parameter estimation procedure is validated through Monte Carlo simulations. The outcomes of these simulations reveal a reduction in biases and mean square errors as sample sizes increase, even for small samples. Two sets of real-engineering data are considered to demonstrate the proposed distribution’s applicability. The performance of the suggested distribution is evaluated using some model selection criteria and goodness-of-fit test statistics. Empirical evidence from these evaluations substantiates that the proposed model outperforms six existing models.