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Demand uncertainty and seller competition are substantial challenges for online platforms. In "To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment," Birge, Chen, Keskin, and Ward analyze whether and how an online platform should offer demand information or price incentives to the sellers participating on the platform. The authors show that, when facing uncertain demand, the platform could be better off by doing nothing—that is, not providing any information or incentives to the sellers. They also develop a strategic reveal-and-incentivize policy for the platform to choose when to start sharing information and offering rewards to coordinate the sellers' pricing. They prove that the strategic reveal-and-incentivize policy achieves near-optimal profit performance for the platform. We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers' sales as well as the customer feature information. We measure the platform's performance by comparing its expected revenue with the full-information optimal revenue, and we design policies that enable the platform to manage information revelation and price-setting incentives. Perhaps surprisingly, a simple "do-nothing" policy does not always exhibit poor revenue performance and can perform exceptionally well under certain conditions. With a more conservative policy that reveals information to make price-setting incentives more effective, the platform can always protect itself from large revenue losses caused by demand model uncertainty. We develop a strategic reveal-and-incentivize policy that combines the benefits of the aforementioned policies and thereby achieves asymptotically optimal revenue performance as T grows large. Funding: This work was supported by Duke University Fuqua School of Business, University of Chicago Booth School of Business, and CUHK Business School. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2023.0363. [ABSTRACT FROM AUTHOR]

The exponential distribution can provide a simple and appealing survival-time model in reliability analysis and life tests. Due to certain experimental designs, time limitations, budgetary requirements, and other reasons, however, only censored data can be obtained. In this paper, we propose a novel estimation procedure for the mean survival time of an exponential distribution under a sequential censoring scheme, which can be treated as a combination of type I censoring and type II censoring. The procedure makes a trade-off between estimation error and sampling cost, using the minimum number of observations. An extensive set of Monte Carlo simulations is conducted to further validate its remarkable performance. To demonstrate the practical applicability, we then implement this newly proposed procedure to assess the reliability of various Backblaze's hard disk models. [ABSTRACT FROM AUTHOR]

Given a gamma population with a known shape parameter α, we develop a general theory for estimating a function g (·) of the scale parameter β with bounded variance. We begin by defining a sequential sampling procedure with g (·) satisfying some desired condition in proposing the stopping rule and show that the procedure enjoys appealing asymptotic properties. After these general considerations, we substitute g (·) with specific functions including the gamma mean, the gamma variance, the gamma rate parameter, and a gamma survival probability as four possible illustrations. For each illustration, Monte Carlo simulations are carried out to justify the remarkable performance of our proposed sequential procedure. This is further substantiated by two real data studies on the survival times of female patients with dementia and on the number of days that the seeds of marigold need to flower, respectively. [ABSTRACT FROM AUTHOR]

Abstract Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep convolutional network based on the Koopman operator (CKNet) is proposed to model non‐linear systems with pixel‐level measurements for long‐term prediction. CKNet adopts an autoencoder network architecture, consisting of an encoder to generate latent states and a linear dynamical model (i.e., the Koopman operator) which evolves in the latent state space spanned by the encoder. The decoder is used to recover images from latent states. According to a multi‐step ahead prediction loss function, the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini‐batch manner. In this manner, gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self‐adaptively tune the latent state space in the training process, and the resulting model is time‐invariant in the latent space. Therefore, the proposed CKNet has the advantages of less inference time and high accuracy for long‐term prediction. Experiments are performed on OpenAI Gym and Mujoco environments, including two and four non‐linear forced dynamical systems with continuous action spaces. The experimental results show that CKNet has strong long‐term prediction capabilities with sufficient precision.

Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep convolutional network based on the Koopman operator (CKNet) is proposed to model non‐linear systems with pixel‐level measurements for long‐term prediction. CKNet adopts an autoencoder network architecture, consisting of an encoder to generate latent states and a linear dynamical model (i.e., the Koopman operator) which evolves in the latent state space spanned by the encoder. The decoder is used to recover images from latent states. According to a multi‐step ahead prediction loss function, the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini‐batch manner. In this manner, gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self‐adaptively tune the latent state space in the training process, and the resulting model is time‐invariant in the latent space. Therefore, the proposed CKNet has the advantages of less inference time and high accuracy for long‐term prediction. Experiments are performed on OpenAI Gym and Mujoco environments, including two and four non‐linear forced dynamical systems with continuous action spaces. The experimental results show that CKNet has strong long‐term prediction capabilities with sufficient precision. [ABSTRACT FROM AUTHOR]

A non-asymptotic procedure of constructing the confidence region of the parameter of the -th order Gaussian autoregressive process AR(p) in the case of an unknown process noise variance is considered. The confidence estimation procedure is based on the martingale property of the numerator of estimate deviation of the least square technique (LST). The results of numerical modeling are presented. [ABSTRACT FROM AUTHOR]

Abstract For radar target tracking with non‐linear measurements, a sequential linear filtering method is proposed in this study, which includes a linear filter based on the position measurements and a linear sequential filter based on the range rate measurement. The linear radar measurement equation in the sequential filter is estimated based on the position filtering result. Furthermore, the feedback information during tracking is adaptively selected according to the quality of the angle measurements. When tracking the manoeuvring target, the proposed sequential linear filtering with adaptive information feedback is combined with the interacting multiple model (IMM) framework, and the IMM filter based on sequential linear filtering with adaptive model probability selection is proposed. Simulation results demonstrate the effectiveness of the proposed algorithms in non‐manoeuvring and manoeuvring target tracking. Compared with traditional algorithms, the tracking performance is improved by the proposed algorithms in both typical non‐manoeuvring and manoeuvring scenes.

In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring. [ABSTRACT FROM AUTHOR]

The navigation of future interplanetary spacecraft will require an increasing degree of autonomy to enhance space system performance. A real-time trajectory determination is of paramount importance to reduce the risks of operations devoted to the exploration of celestial bodies in the solar system and to reduce the dependence and the loading on the ground systems. We present a technique for a sequential estimation of spacecraft orbits through the processing of line-of-sight relative velocity measurements that are acquired by the novel inter-satellite tracking system. This estimation scheme is based on the extended Kalman filter and is tested and validated in a realistic Mars mission scenario. Our numerical simulations suggest that the proposed navigation system can provide accuracies of a few meters in position and a few millimeters per second in velocity. [ABSTRACT FROM AUTHOR]

We consider a seller's dynamic pricing problem with demand learning and reference effects. We first study the case in which customers are loss-averse: they have a reference price that can vary over time, and the demand reduction when the selling price exceeds the reference price dominates the demand increase when the selling price falls behind the reference price by the same amount. Thus, the expected demand as a function of price has a time-varying "kink" and is not differentiable everywhere. The seller neither knows the underlying demand function nor observes the time-varying reference prices. In this setting, we design and analyze a policy that (i) changes the selling price very slowly to control the evolution of the reference price and (ii) gradually accumulates sales data to balance the trade-off between learning and earning. We prove that, under a variety of reference-price updating mechanisms, our policy is asymptotically optimal; that is, its T-period revenue loss relative to a clairvoyant who knows the demand function and the reference-price updating mechanism grows at the smallest possible rate in T. We also extend our analysis to the case of a fixed reference price and show how reference effects increase the complexity of dynamic pricing with demand learning in this case. Moreover, we study the case in which customers are gain-seeking and design asymptotically optimal policies for this case. Finally, we design and analyze an asymptotically optimal statistical test for detecting whether customers are loss-averse or gain-seeking. This paper was accepted by Omar Besbes, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang'an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang'an Airport and Shuangyu Island to prevent and control land subsidence. [ABSTRACT FROM AUTHOR]

The fact that the millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel has sparse support in the spatial domain has motivated recent compressed sensing (CS)-based mmWave channel estimation methods, where the angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using angle dictionary matrices. However, the existing CS-based methods usually obtain the estimation result through one-stage channel sounding that have two limitations: (i) the requirement of large-dimensional dictionary and (ii) unresolvable quantization error. These two drawbacks are irreconcilable; improvement of the one implies deterioration of the other. To address these challenges, we propose, in this paper, a two-stage method to estimate the AoAs and AoDs of mmWave channels. In the proposed method, the channel estimation task is divided into two stages, Stage I and Stage II. Specifically, in Stage I, the AoAs are estimated by solving a multiple measurement vectors (MMV) problem. In Stage II, based on the estimated AoAs, the receive sounders are designed to estimate AoDs. The dimension of the angle dictionary in each stage can be reduced, which in turn reduces the computational complexity substantially. We then analyze the successful recovery probability (SRP) of the proposed method, revealing the superiority of the proposed framework over the existing one-stage CS-based methods. We further enhance the reconstruction performance by performing resource allocation between the two stages. We also overcome the unresolvable quantization error issue present in the prior techniques by applying the atomic norm minimization method to each stage of the proposed two-stage approach. The simulation results illustrate the substantially improved performance with low complexity of the proposed two-stage method. [ABSTRACT FROM AUTHOR]

For radar target tracking with non‐linear measurements, a sequential linear filtering method is proposed in this study, which includes a linear filter based on the position measurements and a linear sequential filter based on the range rate measurement. The linear radar measurement equation in the sequential filter is estimated based on the position filtering result. Furthermore, the feedback information during tracking is adaptively selected according to the quality of the angle measurements. When tracking the manoeuvring target, the proposed sequential linear filtering with adaptive information feedback is combined with the interacting multiple model (IMM) framework, and the IMM filter based on sequential linear filtering with adaptive model probability selection is proposed. Simulation results demonstrate the effectiveness of the proposed algorithms in non‐manoeuvring and manoeuvring target tracking. Compared with traditional algorithms, the tracking performance is improved by the proposed algorithms in both typical non‐manoeuvring and manoeuvring scenes. [ABSTRACT FROM AUTHOR]

Given a Wiener process with unknown and unobservable drift, we try to estimate this drift as effectively but also as quickly as possible, in the presence of a quadratic penalty for the estimation error and of a fixed, positive cost per unit of observation time. In a Bayesian framework, where the unobservable drift is assumed to have a known "prior" distribution, this question reduces to choosing judiciously a stopping time for an appropriate diffusion process in natural scale. We establish structural properties of the solution for the corresponding problem of optimal stopping. In particular, we show that, regardless of the prior distribution, the continuation region is monotonically shrinking in time. Moreover, we provide conditions on the prior distribution that guarantee a one-sided stopping region. Lastly, some concrete prior distributions are studied to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Using wideband signals to improve the resolution performance of forward-looking imaging radar sensors is a very valuable topic. However, the frequency point and angle information are coupled in the array manifold for the wideband array signal, making the high-resolution estimation of range and angle extremely difficult under low complexity. In this paper, we propose a novel algorithm for the wideband forward-looking imaging radar. Firstly, the time delays are estimated by the estimation of signal parameters via rotation invariance techniques (ESPRIT). Then, multiple sets of filters are constructed by using the estimated delays and frequency information. The filters are equipped with sliding window technology to tackle the angle-frequency coupling problem encountered by wideband radar. Through these filters, the received signals are divided into several clusters that correspond to different time delays. Next, the relaxation (RELAX) algorithm is used to estimate the angle of the wideband signal in each cluster. Finally, the range refinement is carried out by using data from all antennas. Compared with the existing algorithms, the proposed Hybrid ESPRIT and RELAX algorithm with clustEring (HERE) decouples the frequency point and angle information for wideband forward-looking imaging radar and then achieves better imaging performance. A series of numerical experiments, including simulation experiments and actual data collected from a wideband forward-looking imaging prototype, have verified the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on the expected number of bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. The encoder and the decoder are synchronized in time. For a class of continuous Markov processes satisfying regularity conditions, we find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. We show that the optimal encoding policy transmits a 1-bit codeword once the process innovation passes one of two thresholds. The optimal decoder noiselessly recovers the last sample from the 1-bit codewords and codeword-generating time stamps, and uses it to decide the running estimate of the current process, until the next codeword arrives. In particular, we show the optimal causal code for the Ornstein-Uhlenbeck process and calculate its distortion-rate function. Furthermore, we show that the optimal causal code also minimizes the mean-square cost of a continuous-time control system driven by a continuous Markov process and controlled by an additive control signal. [ABSTRACT FROM AUTHOR]

In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring.

The navigation of future interplanetary spacecraft will require an increasing degree of autonomy to enhance space system performance. A real-time trajectory determination is of paramount importance to reduce the risks of operations devoted to the exploration of celestial bodies in the solar system and to reduce the dependence and the loading on the ground systems. We present a technique for a sequential estimation of spacecraft orbits through the processing of line-of-sight relative velocity measurements that are acquired by the novel inter-satellite tracking system. This estimation scheme is based on the extended Kalman filter and is tested and validated in a realistic Mars mission scenario. Our numerical simulations suggest that the proposed navigation system can provide accuracies of a few meters in position and a few millimeters per second in velocity.

We describe a control loop over a sequential online algorithm. The control loop either computes or uses the sequential algorithm to estimate the temporal percentiles of any univariate input data sequence. It transforms an input sequence of numbers into output sequence of histograms. This is performed without storing or sorting all of the observations. The algorithm continuously tests whether the input data is stationary, and reacts to events that do not appear to be stationary. It also indicates how to interpret the histograms since their information content varies according to whether a histogram was computed or estimated. It works with parameter-defined, fixed-size small memory and limited CPU power. It can be used for a statistical reduction of a numerical data stream and, therefore, applied to various Internet of Things applications, edge analytics or fog computing. The algorithm has a built-in feasibility metric called applicability. The applicability metric indicates whether the use of the algorithm is justified for a data source: it works for an arbitrary univariate numerical input, but it is statistically feasible only under some requirements, which are explicitly stated here. We also show the results of a performance study, which was executed on the algorithm with synthetic data. [ABSTRACT FROM AUTHOR]

An initial proposal was made to start 30 monkeys in the run-in period of a preclinical translational research study, to have 24 or more animals qualify for randomization in the subsequent treatment period. Based upon data from previous studies, Bayesian posterior prediction indicated that successful enrolment was highly unlikely. At least 67 animals were required to achieve an acceptable posterior predictive probability of success. Importantly, we leveraged these feasibility analyses to introduce our preclinical scientist collaborators to a Bayesian strategy for probability-based decision making. We provided them with a generous helping of graphics to effectively and efficiently illustrate Bayesian concepts and methods. We present our 4P strategy for collaboration with preclinical scientists: patience, persistence, positioning, and privilege. We discuss the alignment of the Bayesian and 4P strategies with goals common to pharmaceutical researchers: scientific innovation; stochastic intelligence and statistical literacy of team members; team collaboration and collegial partnerships; ethical acuity; and fiscal stewardship. Our article is as much about successfully reaching out to preclinical scientists, and introducing them to the Bayesian strategy, as it is about that strategy successfully addressing the animal enrolment question. This article is written at a statistical level accessible to both preclinical scientists and statisticians. [ABSTRACT FROM AUTHOR]

In this study, the observational arc-length effect on orbit determination (OD) for the Korea Pathfinder Lunar Orbiter (KPLO) in the Earth-Moon Transfer phase was investigated. For the OD, we employed a sequential estimation using the extended Kalman filter and a fixed-point smoother. The mission periods, comprised between the perigee maneuvers (PM) and the lunar orbit insertion (LOI) maneuver in a 3.5 phasing loop of the KPLO, was the primary target. The total period was divided into three phases: launch–PM1, PM1–PM3, and PM3–LOI. The Doppler and range data obtained from three tracking stations [included in the deep space network (DSN) and Korea Deep Space Antenna (KDSA)] were utilized for the OD. Six arc-length cases (24 hrs, 48 hrs, 60 hrs, 3 days, 4 days, and 5 days) were considered for the arc-length effect investigation. In order to evaluate the OD accuracy, we analyzed the position uncertainties, the precision of orbit overlaps, and the position differences between true and estimated trajectories. The maximum performance of 3-day OD approach was observed in the case of stable flight dynamics operations and robust navigation capability. This study provides a guideline for the flight dynamics operations of the KPLO in the trans-lunar phase.

In this paper, we continue our research on evaluation of the mean value of the normal distribution with prior information that this parameter is positive and very small. These data are obtained by using a prior exponential distribution with a large intensity parameter. The estimation problem with guaranteed relative error is considered. This issue is more important when small fractions are estimated. In addition to restrictions on the relative error, the procedure must have a given level of d-risk. We suggest a sequential procedure based on the first achievement by posterior probability of estimate reliability of a given level 1–β. The procedure is adapted to the problem of estimating harmful impurities in food products.

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang’an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang’an Airport and Shuangyu Island to prevent and control land subsidence.

This paper presents a direct multi-target localization algorithm for multiple-input multiple-output radar networks employing code division multiple access signals. The proposed algorithm localizes targets one by one whose contributions remain dominant in the received signal after removing the contributions of the localized targets. The sequence of isolating the contributions improves the accuracy of multi-target localization and avoids the computational inefficiency of the maximum likelihood estimator (MLE). Simulation results demonstrate the superiority of the proposed algorithm in comparison with the MLE. [ABSTRACT FROM AUTHOR]

In this study, orbit determination (OD) simulation for the Korea Pathfinder Lunar Orbiter (KPLO) was accomplished for investigation of the observational arc-length effect using a sequential estimation algorithm. A lunar polar orbit located at 100 km altitude and 90° inclination was mainly considered for the KPLO mission operation phase. For measurement simulation and OD for KPLO, the Analytical Graphics Inc. Systems Tool Kit 11 and Orbit Determination Tool Kit 6 software were utilized. Three deep-space ground stations, including two deep space network (DSN) antennas and the Korea Deep Space Antenna, were configured for the OD simulation. To investigate the arc-length effect on OD, 60-hr, 48-hr, 24-hr, and 12-hr tracking data were prepared. Position uncertainty by error covariance and orbit overlap precision were used for OD performance evaluation. Additionally, orbit prediction (OP) accuracy was also assessed by the position difference between the estimated and true orbits. Finally, we concluded that the 48-hr-based OD strategy is suitable for effective flight dynamics operation of KPLO. This work suggests a useful guideline for the OD strategy of KPLO mission planning and operation during the nominal lunar orbits phase.

Modeling correlated or highly stratified multiple‐response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time‐consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active‐learning methods found in the machine‐learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method. [ABSTRACT FROM AUTHOR]

Ground thermal properties (thermal conductivity and heat capacity) are the key parameters for design of borehole heat exchanger (BHE) in ground source heat pump (GSHP). This study presented a novel sequential estimation for ground thermal properties of coaxial BHE by using distributed thermal response test (DTRT) data. Firstly, a comprehensive heat transfer model for coaxial BHE was built to obtain the vertical temperature files. Then a new sensitivity analysis using Spearman correlation method was performed to obtain the correlation between the thermal properties and fluid vertical temperature, determining the estimation sequence. Lastly, according to the sequence, Monte Carlo stochastic method was used to estimate the thermal properties by applying the DTRT data. In addition, the effect of borehole depth and random samples on estimations was investigated. The simulations revealed that the heat input into BHE was mainly used to heat the circulating fluid during the earlier time. It was found that the estimation steps following the thermal conductivity first, then heat capacity got higher precision than other estimation steps with 0.6% and 4% respectively for the two parameters. In addition, the effect of borehole depth on estimation error could be eliminated by using the DTRT data. • DTRT data was interpreted for ground thermal properties estimation. • Sequential parameters estimation was presented for DTRT analysis. • The estimation precision for ground heat capacity was improved to 4%. • Effect of borehole depth on estimate error was eliminated by using DTRT data. [ABSTRACT FROM AUTHOR]

The synthetic aperture radar (SAR) interferometry (InSAR) has been developed for more than 20 years for historical surface deformation reconstruction. In particular, the onboard Sentinel-1/A/B satellite, newly planned NASA-ISRO SAR (NISAR), and Germany Tandem-L will continue to provide unprecedented SAR data with an increased number of acquisitions. However, processing of real-time SAR data has been experiencing challenges regarding the InSAR deformation parameter estimation over a long time with the small baseline subsets (SBAS) InSAR technology. We use sequential adjustment for the estimation of the deformation parameters, which uses Bayesian estimation theory under the least square criteria to inverse long time-series deformation dynamically. Finally, both simulated and real Sentinel-1A SAR data verify the performance of the sequential estimation. It can be regarded as an effective data processing tool in the coming era of SAR big data. [ABSTRACT FROM AUTHOR]

We consider a problem of mean value estimation for a normal distribution with prior knowledge of its randomness and extreme smallness. We continue our investigation presented in article [Volodin et al. Estimation of the mean value for the normal distribution with constrains on d-risk. Lobachevskii J Math. 2018;39:377–387], where the constraints have been made on the absolute estimation error. It is more appropriate to control its relative estimation error, not the absolute. In this article, we consider not only estimates based on a fixed number of observations, but also the sequential procedure of estimation. Both estimators guarantee the given constraints on their d-risks (the so-called d-guarantee procedure). We present a simple method for calculating the minimal sample size that guarantees the given constraints on the d-risk of the relative error when the estimators with uniformly minimal d-risk and Bayesian are applied. A sequential guarantee estimation procedure is also proposed, and the distribution of the corresponding stopping time is illustrated by the results of statistical simulations. As a practical application of the proposed statistical procedures, the problem of estimating the concentration of arsenic in drinking water is considered. [ABSTRACT FROM AUTHOR]

Debiased estimation has long been an area of research in the group testing literature. This has led to the development of several estimators with the goal of bias minimization and, recently, an unbiased estimator based on sequential binomial sampling. Previous research, however, has focused heavily on the simple case where no misclassification is assumed and only one trait is to be tested. In this paper, we consider the problem of unbiased estimation in these broader areas, giving constructions of such estimators for several cases. We show that, outside of the standard case addressed previously in the literature, it is impossible to find any proper unbiased estimator, that is, an estimator giving only values in the parameter space. This is shown to hold generally under any binomial or multinomial sampling plans. [ABSTRACT FROM AUTHOR]

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this study, an enhanced MPP tracking (MPPT) method is proposed utilising the state estimation by the sequential Monte–Carlo (SMC) filtering, which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP to refine the estimation by SMC. Moreover, a quick irradiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change.

We develop three parametric forecasting models that jointly estimate the volume component and the choice component of airline demand. These models—JFM-WPA, JFM-PA, and JFM-WTPU—account for (a) demand volume by considering the average demand of an O–D market, booking curve, seasonality and day-of-the-week indices, and (b) customer behavior by including the maximum willingness-to-pay of the customer and the choice attributes of the available options. We use a mixed logit function to formulate the willingness-to-pay and customer choice behavior. JFM-WPA excludes price from the set of choice attributes. JFM-PA considers price as one of the choice attributes. The utilities of maximum willingness-to-pay and choices are combined in JFM-WTPU. We propose a sequential method to estimate the forecasting models. We utilize the data generated by Airline Planning and Operations Simulator (APOS) from real airline historic data. We compare the models and present the results. These demand models can also be used as an input for optimization models, for joint seat allocation and pricing. [ABSTRACT FROM AUTHOR]

• Rigorously develop the MMV VALSE (MVALSE). • Provide the uncertainty of the frequency estimates. • Show the relationship between MVALSE and VALSE. • Extend MVALSE to perform sequential estimation. Line spectral estimation (LSE) with multiple measurement vector (MMV) is studied utilizing the Bayesian variational inference. Motivated by the recent grid-less variational line spectral estimation (VALSE) method, we develop the MMV VALSE (MVALSE). The MVALSE shares the advantages of the VALSE method, such as automatically estimating the model order, noise variance, weight variance, and providing the uncertainty of the frequency estimates. The MVALSE can be viewed as applying the VALSE with single measurement vector to each snapshot, and combining the intermediate data appropriately. Furthermore, the MVALSE is developed to perform sequential estimation. Numerical results demonstrate the effectiveness of the MVALSE method, compared to the state-of-the-art MMV methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of forecasting the magnitude of the strongest aftershock starting from a certain instant of time in the future. This problem is topical since the strong aftershocks that occur later against a background of less frequently repeating shocks are less expected and thus pose an independent hazard. At the same time, the magnitudes of the strongest aftershocks decrease with time after the main shock. The purpose of accurately forecasting them is to minimize the underestimation or overestimation of the magnitude of future risks. In this study, the aftershock process is represented by the superposition of the Gutenberg–Richter and Omori–Utsu laws whose parameters are estimated by the Bayess method using the data on the aftershocks that have already occurred up to a given time point and the a priori information about the probable values of the parameters. This significantly improves the forecast compared to the estimates that are based on the magnitude of the main shock alone. The quality of forecasting is estimated relative to Båth's dynamic law with the use of two independent criteria. The first criterion is based on the similarity estimates, and the second, on the error diagram. [ABSTRACT FROM AUTHOR]

We propose an optimal sequential methodology for obtaining confidence intervals for a binomial proportion $\theta$. Assuming that an independent and identically distributed sequence of Bernoulli ($\theta$) trials is observed sequentially, we are interested in designing: 1) a stopping time $T$ that will decide the best time to stop sampling the process and 2) an optimum estimator $\hat{{\theta}}_{{T}}$ that will provide the optimum center of the interval estimate of $\theta$. We follow a semi-Bayesian approach, where we assume that there exists a prior distribution for $\theta$ , and our goal is to minimize the average number of samples while we guarantee a minimal specified coverage probability level. The solution is obtained by applying standard optimal stopping theory and computing the optimum pair $(T,\hat{{\theta }}_{{T}})$ numerically. Regarding the optimum stopping time component $T$ , we demonstrate that it enjoys certain very interesting characteristics not commonly encountered in solutions of other classical optimal stopping problems. In particular, we prove that, for a particular prior (beta density), the optimum stopping time is always bounded from above and below; it needs to first accumulate a sufficient amount of information before deciding whether or not to stop, and it will always terminate before some finite deterministic time. We also conjecture that these properties are present with any prior. Finally, we compare our method with the optimum fixed-sample-size procedure as well as with existing alternative sequential schemes. [ABSTRACT FROM AUTHOR]

We develop fixed size confidence regions for estimating the fixed and random effects parameters of the mixed effects logistic regression model. This model applies to, among others, the study of the effects of covariates on a dichotomous response variable when subjects are sampled in clusters. Two sequential procedures are developed to estimate with a prescribed accuracy (confidence level) and fixed precision the set of fixed and random effects parameters and linear transformations of these parameters, respectively. We show that the two procedures are asymptotically consistent (i.e., the coverage probability converges to the nominal confidence level) and asymptotically efficient (i.e., the ratio of the expected random sample size to the unknown best fixed sample size converges to 1) as the width of the confidence region converges to 0. Suggestions to improve the performance of the procedures are provided based on Monte Carlo simulation and illustrated through a longitudinal clinical trial data. [ABSTRACT FROM AUTHOR]

Unless a segregated airspace and the corresponding clearances can be afforded, flight testing of remotely piloted aircraft is often done near the ground and within visual line-of-sight. In addition to the increased exposure to turbulence, this setup also limits the available time for test manoeuvres on each pass, especially for subscale demonstrators with a relatively high wing loading and flight speed. A suitable testing procedure, efficient excitation signals and a robust system identification method are therefore fundamental. Here, the authors use ground-based flight control augmentation to inject multisine signals with low correlation between the different inputs. Focusing on initial flight-envelope expansion, where linear regression is common, this paper also describes the improvement of an existing frequency-domain method by using an instrumental variable (IV) approach to better handle turbulence and measurement noise and to enable real-time identification analysis. Both simulations and real flight tests on a subscale demonstrator are presented. The results show that the combination of multisine input signals and the enhanced frequency-domain method is an effective way of improving flight testing of remotely piloted aircraft in confined airspace.