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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]

Summary Multicollinearity results in inflation in the variance of the ordinary least squares estimators due to the correlation between two or more independent variables (including the constant term). A widely applied solution is to estimate with penalised estimators such as the ridge estimator, which trade off some bias in the estimators to gain a reduction in the variance of these estimators. Although the variance diminishes with these procedures, all seem to indicate that the inference and goodness of fit are controversial. Alternatively, the raise regression allows mitigation of the problems associated with multicollinearity without the loss of inference or the coefficient of determination. This paper completely formalises the raise estimator. For the first time, the norm of the estimator, the behaviour of the individual and joint significance, the behaviour of the mean squared error and the coefficient of variation are analysed. We also present the generalisation of the estimation and the relation between the raise and the residualisation estimators. To have a better understanding of raise regression, previous contributions are also summarised: its mean squared error, the variance inflation factor, the condition number, adequate selection of the variable to be raised, the successive raising, and the relation between the raise and the ridge estimator. The usefulness of the raise regression as an alternative to mitigate multicollinearity is illustrated with two empirical applications. [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]

Several recent works have tackled the estimation issue for the unidimensional four‐parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time‐consuming. In this paper, a mixture‐modelling‐based Bayesian MH‐RM (MM‐MH‐RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM‐MH‐RM algorithm to the original MH‐RM algorithm, two simulation studies and an empirical example demonstrated that the MM‐MH‐RM algorithm possessed the benefits of the mixture‐modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM‐MH‐RM algorithm are available in the online appendix. [ABSTRACT FROM AUTHOR]

Image registration is a widely used tool for matching two images of the same scene with one another. In the literature, several image registration techniques are available to register rigid‐body and non‐rigid‐body transformations. One such important transformation is zooming. There are very few feature‐based methods that address this particular problem. These methods fail miserably when there are only a limited number of point features available in the image. This paper proposes a feature‐based approach that works with a feature that is readily available in almost all images, for registering two images of the same image object where one is a zoomed‐in version of the other. In the proposed method, we first detect the possible edge points which we consider as features in both the reference and the zoomed image. Then, we map these features of the reference and the zoomed image with one another and find the relationship between them using a mathematical model. Finally, we use the relationship to register the zoomed‐in image. This method outperforms some of the state‐of‐the‐art methods in many occasions. Several numerical examples and some statistical properties justify that this method works well in many applications. [ABSTRACT FROM AUTHOR]

The geometric process is one of the important simple monotonic processes with a positive ratio parameter in the theory of stochastic processes. Simply, it can be thought of as a generalization of the renewal process (RP). In the current paper, we mainly study the geometric process with the Hjorth marginal distribution, with parameters θ and λ, for being able to model the successive inter‐arrival times with a trend. We first examine inference problem for the mentioned process from different perspectives and obtain the different estimators of its parameters by employing different estimation methods such as maximum likelihood, modified moments, modified maximum spacing, and modified least‐squares. The efficiencies of these estimators are compared via a series of extensive simulation studies in the paper. Further, we give also a discrimination statistic for discriminating among geometric processes with the Hjorth distribution and its alternatives. This is quite important to select the optimal geometric process model for data. Finally, a modeling study by using the geometric process with the Hjorth distribution is provided in detail to display its effectiveness to model the reliability data sets. [ABSTRACT FROM AUTHOR]

Inflated distributions are applied in various fields, including insurance, traffic networks and survival analyses. First, they are defined by a baseline discrete distribution, and then, extra masses are added to some points of interest, called inflated points, to achieve more flexible models for data analyses. The baseline distribution is arbitrary and application dependent. Here, the rich family of power series distributions is considered as the baseline, which includes various common discrete distributions such as Poisson, negative binomial, multinomial and logarithmic series distributions. This paper deals with an extension of previous works in two directions. The former is an extension of the univariate inflated distributions to multivariate ones, and the latter is the generalization of the inflated points from the zero single point to the set k=0,1,…. Under this setting, various inflated distributions in the literature fall into the proposed family of distributions. These extensions make the proposed model flexible and practically useful in data analyses. To do this, the problem of estimating parameters with various approaches as well as hypotheses testing is studied in detail. Multivariate‐generalized linear models with inflated multivariate discrete responses are also discussed. To assess the performance of the proposed family of inflated distributions, simulation studies are conducted, and a real data set on an Australian health survey study is also analysed. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of determining optimal convergence rates of Galerkin approximations to infinite dimensional operator Riccati equations (OREs). Optimal rates are obtained for a class of abstract distributed parameter systems evolving in an infinite dimensional Hilbert space. These general results are then applied to systems modeled by partial differential equations that generate compact and analytic semigroups. The estimates apply to distributed control and observation of classical parabolic equations and to certain vibration problems with sufficiently strong damping. The ORE is formulated as an equivalent operator‐valued Bochner integral equation and the Brezzi–Rappaz–Raviart theorem is used to obtain convergence rates. First we establish smoothing property and bounds for the solutions of the infinite dimensional ORE. Then it is shown that, under sui\ assumptions on the coefficients and domain geometry, the hp‐finite element approximations of the classical solution converges on the order of Ohk+1. Furthermore, these optimal error bounds are shown to hold for the functional gains that define observer and control gain operators. We provide numerical examples that corroborate the theoretical convergence rates. [ABSTRACT FROM AUTHOR]

In oncology clinical trials, response‐based endpoints (time to response, objective response, duration of response [DOR]) are commonly used to detect therapeutic effect to support proof‐of‐concept or submission decisions. The restricted mean DOR (RMDOR) was recently proposed as a composite nonparametric method to efficiently quantify the treatment effect related to tumor reductions, which offers an intuitive way to perform statistical inference in cross‐arm comparison and has since been applied in some Phase III studies. In this paper, we provide further technical details and asymptotic properties of the RMDOR method and discuss the selection of the truncation time. A simulation study is conducted comparing the performance of the proposed method with existing standard methods in hypothesis testing and quantification of treatment efficacy. We use two oncology Phase III examples to illustrate the method. An R package PBIR and a SAS macro are available to perform statistical inference based on the RMDOR. [ABSTRACT FROM AUTHOR]

Bycatch remains one of the most important issues in the world's fisheries so its estimation and reporting have been highlighted in many international, regional and jurisdictional guidelines and policies. This paper describes a simple methodology to estimate jurisdictional discards, using Australia's first national bycatch report as a case study. The methodology involves: (a) identifying annual landings for all fisheries and methods; (b) deriving retained:discard ratios for each; (c) where ratios are lacking, using substitute ratios from similar fisheries; (d) applying the ratios from (b) and (c) to the data from (a) to obtain totals; and (e) scoring the quality of the discard information using the US Tier Classification System weighted by estimated discard levels. The results for Australia revealed that, during the last decade, commercial fisheries annually discarded 42.5% of what was caught (87,983t). 70% came from just eight fisheries/methods with 30% coming from the other 299. The Queensland East Coast Prawn Trawl fishery contributed 28.5% of the national total. The quality of discard information was reasonable across most jurisdictions, with a national score of 59.1%. The best quality data came from the Commonwealth due to its observer and (more recent) Electronic Monitoring programmes. Those data also showed that fishers' logbook information under‐estimated levels of discards (determined from observer data) by 89.7%. This paper provides: (a) the means to develop benchmarks in bycatch management and estimation against which jurisdictions can be compared and performances tracked; and (b) for Australia, priority areas for management intervention to reduce discarding and improve its monitoring. [ABSTRACT FROM AUTHOR]

Summary: In order to correct and avoid channel error, fountain codes were the best solution by limiting feedback channels and reducing energy consumption. Multi‐hops transmission is the principal limitation of the deployment and the use of these codes. Indeed, relayed transmission conducts to the generation of useless data, named overflow leading to a waste of energy, the most critical issue, and the big challenge in WSN. In this paper, based on a clustered architecture and estimation, we consider a distributed estimation scheme composing of sensor members and the cluster head. In order to reduce the number of a useless encoded packet generated as well as the impact of the overflow, we determine the optimal minimal number of encoded packets needed for data decoding. Sensor observations are encoded using fountain codes, and then messages are collected at the cluster head where a final estimation is provided within learning method. Then messages are collected at the cluster head where a final estimation is provided with a classification based on Bayes rule. The main goal of this paper is to determine the number of encoded packets by exploiting the classification model for fountain data estimation to minimize the overflow and extend the network lifetime. [ABSTRACT FROM AUTHOR]

Summary: This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models. [ABSTRACT FROM AUTHOR]

The 2-in-1 adaptive design allows seamless expansion of an ongoing Phase II trial into a Phase III trial to expedite a drug development program. Since its publication, it has generated a lot of interest. So far, most of the related research focused on type I error control. Similar to most adaptive designs, 2-in-1 design could also pose a great challenge on estimation of treatment effect due to the data-driven adaptation. In addition, the use of intermediate endpoint for interim adaptive decision-making is a less well-studied field. In this paper, we investigate the bias and variances in estimation for 2-in-1 design and some of its extensions, and propose some bias-adjusted estimators for 2-in-1 design. The properties of the proposed estimators are further studied theoretically and/or numerically, so as to provide guidance on how to interpret the estimated treatment effect of 2-in-1 design. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the bulk of Internet traffic uses Transmission Control Protocol (TCP) for reliable transmission. But the standard TCP's performance is very poor in high‐speed networks (HSNs), and hence, the core gigabytes links are usually underutilization. This problem has roots in conservative nature of TCP, especially in its Additive Increase Multiplicative Decrease (AIMD) phase. In other words, since TCP cannot figure out precisely the congestion status of the network, it follows a conservative strategy to keep the network from overwhelming. We believe that precisely congestion estimation in the network can solve this problem by avoiding unnecessary conservation. To this end, this paper proposes an algorithm, which considers packet loss and delay information jointly and employs a probabilistic approach to accurately estimation of congestion status in the network. To examine the proposed scheme performance, extensive simulations have been performed in the NS‐2 environment. Simulation results reveal that the proposed algorithm has better performance than existing algorithms in terms of bottleneck utilization, stability, throughput, and fairness. [ABSTRACT FROM AUTHOR]

Gilmour, Thompson, and Cullis (Biometrics, 1995, 51, 1440) presented the average information residual maximum likelihood (REML) algorithm for efficient variance parameter estimation in the linear mixed model. That paper dealt specifically with traditional variance component models, but the algorithm was quickly applied to more general models and implemented in several REML packages including ASReml (Gilmour et al., Biometrics, 2015, 51, 1440). This paper outlines the theory with respect to these more general models, describes the main issues encountered in fitting these models and how they have been addressed in the ASReml software. The issues covered are the basics steps in the implementation of the algorithm, keeping parameters within the parameter space, maximizing sparsity, avoiding issues associated with unstructured variance matrices by using the factor‐analytic structure and handling singularities in marker‐based relationship matrices and current work. [ABSTRACT FROM AUTHOR]

Over 70 million individuals are infected with hepatitis C virus (HCV) worldwide. Yet most prevalence data are in the adult population, with little focus on paediatrics, partially due to the scarcity of public data. The objective of this paper is to examine HCV prevalence in children by estimating prevalence rates among women, given the assumption that most cases are vertically transmitted. Between 2001 and 2017, maternal HCV infection affected ~ 0.24% of all births, with prevalence increasing by at least 261%. On average, approximately 0.01% of the total number of live births were infected with HCV, with a 245% increase in the number of children born with the infection. HCV epidemiology has evolved, with women of childbearing age representing a greater proportion of infected individuals in the United States, and infants born to infected mothers being at risk. We therefore recommend a greater public health focus of HCV on the paediatric population. [ABSTRACT FROM AUTHOR]

This paper presents a new method for estimating the amount of an artefact class in use at a given moment in the past from a random assemblage of archaeological finds. The method is based on the use of simulation, since an analytical solution is computationally impractical. Estimating the number of artefacts in use at any time t is shown to follow a Poisson distribution, which allows for credible intervals to be established using the Jeffreys prior. This estimator works from minimal assumptions about the dating and duration of finds, as well as the intensity of collection, and is applied to coinage from four Roman‐period sites excavated by the Roman Peasant Project (2009–14). The result provides an estimation of the abundance of material according to an interval of certainty. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an L1 adaptive output‐feedback descriptor is designed for multivariable nonlinear systems with measurement noises. If the system is detectable, noises are bounded, and some rank conditions are satisfied, an L1 adaptive output‐feedback descriptor is constructed to asymptotically estimate states, nonlinear uncertainties, and measurement noises at the same time deliver a good tracking performance. The original system is augmented with all the system states and measurement noises; two design parameters provide additional degrees of freedom. The freedom of selecting these parameters allows us to choose the derivative gain to reduce the noise amplification and the proportional gain to ensure the stability of the estimated error dynamics. An adaptive law will update the adaptive parameters that represent the uncertainty estimates such that the estimation error between the predicted state and the real state is driven to zero at every integration time step. Of course, neglection of the unknowns for solving the error dynamic equations will introduce an estimation error in the adaptive parameters. The magnitude of this error can be lessened by choosing a proper sampling time. The two design parameters and adaptive law guarantee the performance bounds for the estimation errors, both states and control signals. A control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. Numerical examples are given to illustrate the design procedures, and the simulation results demonstrate the availability and feasibility of the developed framework. [ABSTRACT FROM AUTHOR]

The application of lithium‐ion batteries especially for electric vehicles has been limited by the factors of safety, lifetime, charging time, and cost. One of the principal limitations is that the performance of Li‐ion batteries drops intensely in a cold environment. Cold environment dramatically reduces the available capacity of the batteries and increases its internal impedance at the same time. Therefore, the estimation of state‐of‐health is of great importance in battery performance evaluation and lifetime prediction. Furthermore, the heating methods need to be developed to ensure that batteries work in abnormal temperature conditions. This paper conducts a comprehensive review specifically on the poor performance of lithium‐ion cells under severe conditions. The content contains three sections. First, a comprehensive study on the aging mechanisms of lithium‐ion batteries at cold temperatures is undertaken. Second, the estimation methods of the health state of the batteries are conducted, which is vital to understand the fundamentals and quantify the performance and aging effects for lithium‐ion batteries. Third, the heating methods are classified and studied in detail to reduce the degradation mechanism and promote the performance of lithium‐ion batteries under sub‐zero conditions. [ABSTRACT FROM AUTHOR]

Flow reactors heated by microwave irradiation attract attention. The reactors are suitable for difficult synthesis processes due to rapid heating and cooling, and easy pressurization. In order to predict the quality of the product, it is appropriate to estimate the outlet conditions of the reactor. In this paper, the outlet temperature of the flow direction is estimated by using the flow condition and dynamic thermal energy balance of the reactor. The outlet temperature of the flow direction is estimated by using the flow condition and dynamic thermal energy balance of a flow reactor heated by microwave irradiation. The temperature estimation can contribute to estimate the quality of the product. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a novel fault estimation strategy is proposed for a network of dynamical systems at a supervisory monitoring level. The network nodes include linear and Lipschitz nonlinear dynamics and time-varying coupling strength. The aim is to enhance the autonomy level of this class of systems by means of this inherently robust, nonlinear strategy based on sliding mode ideas. The faults are reconstructed from the equivalent output error injection signal which is used to maintain sliding. A key facet of the strategy is that the synthesis of the sliding mode observer for the network depends solely on the dynamics of an individual node of the network. The theoretical results developed in the paper are demonstrated with an example consisting of a network of pendulums. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The need to reduce emissions and improve mobility in overcrowded cities is promoting the use of bicycles as transportation means. Bicycles have a small footprint, are easy to use, and cost effective. The introduction of modern electric bicycles has also widened the user base, extending the reach of bicycles as a commuter's option. Electric bicycles, in order to meet regulation standards, need sensors that are not usually available on muscular bicycles, like torque or cadence sensors. In this paper, we develop a cadence estimation strategy based on the wheel speed encoder only, thus allowing to remove the cadence sensor. Specifically, we propose 2 approaches, ie, a direct cadence estimate and an indirect one via gear ratio estimate. Both estimation problems are shown to be equivalent to a frequency tracking problem, which can be solved by Kalman filtering. The final algorithm embeds a logic supervisor that guarantees the reliability of the procedure in all working conditions, including freewheeling. The whole analysis and development are based upon a thorough experimental campaign using an instrumented bicycle. [ABSTRACT FROM AUTHOR]

This paper investigates the attack-resilient state estimation problem for linear systems with adversarial attacks and unknown inputs, where the upper bound of the unknown inputs is unknown. It is assumed that the attacker has limited resources and can only manipulate a certain number of sensors. In most of the existing observer design approaches for the systems with unknown inputs even in the absence of attacks, the observer matching condition should be satisfied. To overcome this restriction, a novel switched observer is proposed, where the matched unknown inputs will be completely compensated by means of the outputs and the mismatched part will be suppressed in terms of L2-gain rejection property. Meanwhile, the observer can provide an attack-resilient state estimation. Compared with the existing results, the proposed observer can guarantee that the resulting observer error system is stable with unknown input attenuation level γ that can be optimized. Finally, a simulation example of an unmanned ground vehicle is provided to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with the constrained [ABSTRACT FROM AUTHOR]

This study is concerned with the robust nonlinear filtering problem for nonlinear discrete-time stochastic system with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts. The focus of this paper is to design a filter with predictor-corrector structure such that the upper bound on the state estimation error variance is minimized in the presence of multiplicative noise, unknown external disturbances, and packet dropouts. Thus, a robust nonlinear filter based on the method to obtain the upper bound on variances of multiplicative noises, unknown disturbances, and packet dropouts is designed. Further stability analysis shows that the proposed filter has robustness against multiplicative noises, unknown external disturbances, and packet dropouts. Simulation results show that the proposed filter is more effective than extended Kalman filter and other robust extended Kalman filter. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper we propose a new stationary first-order non-negative integer valued autoregressive process with geometric marginals based on a generalised version of the negative binomial thinning operator. In this manner we obtain another process that we refer to as a generalised stationary integer-valued autoregressive process of the first order with geometric marginals. This new process will enable one to tackle the problem of overdispersion inherent in the analysis of integer-valued time series data, and contains the new geometric process as a particular case. In addition various properties of the new process, such as conditional distribution, autocorrelation structure and innovation structure, are derived. We discuss conditional maximum likelihood estimation of the model parameters. We evaluate the performance of the conditional maximum likelihood estimators by a Monte Carlo study. The proposed process is fitted to time series of number of weekly sales (economics) and weekly number of syphilis cases (medicine) illustrating its capabilities in challenging cases of highly overdispersed count data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new first-order non-negative integer-valued autoregressive [INAR(1)] process with Poisson–geometric marginals based on binomial thinning for modeling integer-valued time series with overdispersion. Also, the new process has, as a particular case, the Poisson INAR(1) and geometric INAR(1) processes. The main properties of the model are derived, such as probability generating function, moments, conditional distribution, higher-order moments, and jumps. Estimators for the parameters of process are proposed, and their asymptotic properties are established. Some numerical results of the estimators are presented with a discussion of the obtained results. Applications to two real data sets are given to show the potentiality of the new process. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we propose a new method for identification of linear time-invariant systems and estimation of unknown periodic disturbances. We assume that a disturbance to be considered here is unknown except its period. In this method, by stacking not only the unknown coefficients of a mathematical model but also the unknown disturbance as a parameter vector, we modify the equation error method so as to treat the unknown disturbance in the identification scheme. In particular, we also explain how the periodicity of the disturbance is utilized. In order to show the validity of this method, we give an illustrative numerical example. [ABSTRACT FROM AUTHOR]

The multivariate adaptive exponentially weighted moving average control chart (MAEWMA) can detect shifts of different sizes while diminishing the inertia problem to a large extent. Although it has several advantages compared to various multivariate charts, previous literature has not considered its performance when the parameters are estimated. In this study, the performance of the MAEWMA chart with estimated parameters is studied while considering the practitioner-to-practitioner variation. This kind of variation occurs due to using different Phase I samples by different practitioners in estimating the unknown parameters. The simulation results in this paper show that estimating the parameters results in extensively excessive false alarms and as a result a large number of Phase I samples is needed to achieve the desired in-control performance. Using small number of Phase I samples in estimating the parameters may result in an in-control ARL distribution that almost completely lies below the desired value. To handle this problem, we strongly recommend the use of a bootstrap-based algorithm to adjust the control limit of the MAEWMA chart. This algorithm enables practitioners to achieve, with a certain probability, an in-control ARL that is greater than or equal to the desired value while using the available number of Phase I samples. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study. [ABSTRACT FROM AUTHOR]

Senegal has a high rate of tuberculosis and a low HIV seropositivity rate and aspergilloma, life-threatening fungal infections, dermatophytosis and mycetoma have been reported in this study. All published epidemiology papers reporting fungal infection rates from Senegal were identified. Where no data existed, we used specific populations at risk and fungal infection frequencies in each to estimate national incidence or prevalence. The results show that tinea capitis is common being found in 25% of children, ~1.5 million. About 191 000 Senegalese women get recurrent vaginal thrush, ≥4 times annually. We estimate 685 incident cases of chronic pulmonary aspergillosis ( CPA) following TB and prevalence of 2160 cases. Asthma prevalence in adults varies from 3.2% to 8.2% (mean 5%); 9976 adults have allergic bronchopulmonary aspergillosis ( ABPA) and 13 168 have severe asthma with fungal sensitisation ( SAFS). Of the 59 000 estimated HIV-positive patients, 366 develop cryptococcal meningitis; 1149 develop Pneumocystis pneumonia and 1946 develop oesophageal candidiasis, in which oral candidiasis (53%) and dermatophytosis (16%) are common. Since 2008-2010, 113 cases of mycetoma were diagnosed. In conclusion, we estimate that 1 743 507 (12.5%) people in Senegal suffer from a fungal infection, excluding oral candidiasis, fungal keratitis, invasive candidiasis or aspergillosis. Diagnostic and treatment deficiencies should be rectified to allow epidemiological studies. [ABSTRACT FROM AUTHOR]

The paper introduces a functional time series (lagged) regression model. The impulse-response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric condition on the temporal dependence of the input series. Since the data are infinite-dimensional, the estimation involves a spectral-domain dimension-reduction technique. Consistency of the estimators is established under general data-dependent assumptions on the rate of the dimension-reduction parameter. Their finite-sample performance is evaluated by a simulation study that compares two ad hoc approaches to dimension reduction with an alternative, asymptotically justified method. [ABSTRACT FROM AUTHOR]

SUMMARY This paper deals with [ABSTRACT FROM AUTHOR]

This paper deals with estimation of total output of solar photovoltaic (PV) generators widely distributed in future power systems. In the proposed framework of estimation, the estimated signal in each frequency band is a linear combination of observed signals. The observation gain for each observed signal in each frequency band is designed through following two steps. First, an estimation error function is defined using assumed root mean square of output of PVs, predicted distribution of future PVs in the area, and correlation coefficients, which are identified in advance based on actual synchronized measurements of PVs. Second, observation gain in each frequency band is designed so as to minimize the estimation error function. The appropriateness of the proposed framework of estimation is demonstrated in small- and large-scale examples. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we consider the observer design problem for a class of observable linear systems perturbed by nonlinear, time-varying terms. Our design methodology is based on a canonical form, similar to canonical forms used elsewhere in the literature, that allows the nonlinearities to be dominated using high gain. We show that linear state and output transformations to this canonical form exist if, and only if, the data of the system satisfies a certain admissibility property. Moreover, the appropriate transformations can easily be constructed using available tools. We furthermore show that, if a system does not satisfy the admissibility property, it may be possible to extend it with an invertible output filter that makes the data of the extended system admissible. We refer to the problem of constructing such a filter as the output shaping problem and introduce an algorithm that solves the problem whenever it is solvable. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY An improved algorithm is presented for a signal's direction of arrival estimation to reduce the computational complexity of the existing modified multiple signal classification (MMUSIC) algorithm. In this paper, FFT algorithm is introduced to narrow the searching range of the direction angle. Afterwards, a compromise approach-based SVD and orthogonal-triangular decomposition takes the place of traditional double SVDs. Then, during matrix calculation, the noise subspace is achieved for the signal's direction of arrival. Finally, the system simulation demonstrates the efficiency and reliability of this novel MMUSIC algorithm. Compared with the existing algorithm, MMUSIC effectively saves the computational complexity with the approximation estimation performance. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The primary goal of robust parameter design (RPD) is to determine the optimum operating conditions that achieve process performance targets while minimizing variability in the results. To achieve this goal, typical approaches to RPD problems use ordinary least squares methods to obtain response functions for the mean and variance by assuming that the experimental data follow a normal distribution and are relatively free of contaminants or outliers. Consequently, the most common estimators used in the initial tier of estimation are the sample mean and sample variance, as they are very good estimators when these assumptions hold. However, it is often the case that such assumed conditions do not exist in practice; notably, that inherent asymmetry pervades system outputs. If unaccounted for, such conditions can affect results tremendously by causing the quality of the estimates obtained using the sample mean and standard deviation to deteriorate. Focusing on asymmetric conditions, this paper examines several highly efficient estimators as alternatives to the sample mean and standard deviation. We then incorporate these estimators into RPD modeling and optimization approaches to ascertain which estimators tend to yield better solutions when skewness exists. Monte Carlo simulation and numerical studies are used to substantiate and compare the performance of the proposed methods with the traditional approach. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many methods are available for computing a confidence interval for the binomial parameter, and these methods differ in their operating characteristics. It has been suggested in the literature that the use of the exact likelihood ratio (LR) confidence interval for the binomial proportion should be considered. This paper provides an evaluation of the operating characteristics of the two-sided exact LR and exact score confidence intervals for the binomial proportion and compares these results to those for three other methods that also strictly maintain nominal coverage: Clopper-Pearson, Blaker, and Casella. In addition, the operating characteristics of the two-sided exact LR method and exact score method are compared with those of the corresponding asymptotic methods to investigate the adequacy of the asymptotic approximation. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper discusses the use of H-infinity approximation for the online adaptation of PID controller parameters. For a frequency loop-shaping control objective, it is possible to adapt the PID parameters directly with linear model estimation algorithms. Standard least squares algorithms are common solutions for this problem, but their estimates exhibit a well-known strong dependence on the properties of the excitation. This drawback becomes more pronounced for systems where the modeling mismatch is large, as is frequently the case in PID control. In an alternative formulation of the estimation problem, we use a filter-bank to decompose the error signal to different components and minimize approximately the H-infinity norm of the sensitivity-weighted error operator. This approach results in a more consistent estimate of the optimal PID parameters, at the expense of higher excitation requirements. It also allows for the computation of a 'health indicator' to describe the confidence in the estimated parameters. The practical implication of this observation is that PIDs can be tuned more reliably, even in cases of large mismatch between the target and the feasible loop shapes. It also suggests a general theme where a min-max optimization of an operator error provides an advantage over signal error optimization. The key aspects of the algorithm are illustrated by numerical examples. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Outcomes in clinical trials may be affected by the choice of treatment that participants might make, if they were indeed allowed to choose (a so-called selection effect), and by whether they actually receive their preferred treatment (a preference effect). Selection and preference effects can be important, but they cannot be estimated in the conventional trial design. An alternative approach is the two-stage randomised trial, in which participants are first randomly divided into two subgroups. In one subgroup, participants are randomly assigned to treatments, while in the other, participants are allowed to choose their own treatment. This approach yields estimates of the direct treatment effect, and of the preference and selection effects. The latter two provide insight that goes considerably beyond what is possible in the standard randomised trial. In this paper, we determine the optimal proportion of participants who should be allocated to the choice subgroup. The precision of the estimated selection, preference and treatment effects are functions of: the total sample size; the proportion of participants allocated to choose their treatment; the variances of the outcome; the proportions of participants who select each treatment in the choice group; and the selection, preference and treatment effects themselves. We develop general expressions for the optimum proportion of participants in the choice group, depending on which effects are of primary interest. We illustrate the results with trial data comparing alternative clinical management strategies for women with abnormal results on cervical screening. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodic time–series models. Under simple conditions that are straightforward to check, we establish the strong consistency, the rate of strong convergence and the asymptotic normality of a general class of estimators that includes LSE, MLE and some M-type estimators. As an application, we verify the assumptions for the long-memory fractional ARIMA model. Other examples include the GARCH(1,1) model, random coefficient AR(1) model and the threshold MA(1) model. [ABSTRACT FROM AUTHOR]

In this paper, a new bivariate generalized Poisson distribution (GPD) that allows any type of correlation is defined and studied. The marginal distributions of the bivariate model are the univariate GPDs. The parameters of the bivariate distribution are estimated by using the moment and maximum likelihood methods. Some test statistics are discussed and one numerical data set is used to illustrate the applications of the bivariate model. [ABSTRACT FROM AUTHOR]

In recent years, many maximum likelihood (ML) blind estimators have been proposed to estimate timing and frequency offsets for orthogonal frequency division multiplexing (OFDM) systems. However, the previously proposed ML blind estimators utilizing cyclic prefix do not fully characterize the random observation vector over the entire range of the timing offset and will significantly degrade the estimation performance. In this paper, we present a global ML blind estimator to compensate the estimation error. Moreover, we extend the global ML blind estimator by accumulating the ML function of the estimation parameters to achieve a better accuracy without increasing the hardware or computational complexity. The simulation results show that the proposed algorithm can significantly improve the estimation performance in both additional white Gaussian noise and ITU-R M.1225 multipath channels. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To conduct electric power transactions effectively and to operate a power system efficiently while maintaining reliability under the deregulated environment, it is required that ATC (Available Transfer Capability) be calculated at high speed and with reasonable precision. In order to address this issue, in this paper, an Artificial Neural Network-based estimation method for evaluating Maximum Transmission Capability (MTC), which is a key step but also a highly time consuming process in ATC, is proposed. It is confirmed through simulation studies that the proposed method is capable of estimating MTC (ATC) with high speed and sufficient precision. Furthermore, the authors examined the reduction of calculation time at learning by using the transient stability index. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 66–73, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20781 [ABSTRACT FROM AUTHOR]

The unevenness of contact lines is one of the factors that considerably influences the dynamic characteristics of current collection systems. In order to analyze the influence of the contact line unevenness on the fluctuation of the contact force between contact line and pantograph, an instrument was devised to measure the unevenness of contact lines accurately and continuously. The instrument consists of two pairs of laser sensors for the purpose of reducing measurement errors by vertical vibration of the measurement vehicle. This paper describes the measuring method of contact line unevenness, reports some examples of the measurement of real lines, and proposes a method to evaluate the conditions of overhead equipment. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 160(2): 77–85, 2007; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/eej.20209 [ABSTRACT FROM AUTHOR]

This paper investigates an efficient estimation method for a cointegrating regression model with structural change. Our proposal is that we first estimate the break point by minimizing the sum of squared residuals and then, by replacing the break fraction with the estimated one, we estimate the regression model by the canonical cointegrating regression (CCR) method proposed by Park [ Econometrica (1992 ) Vol. 60, pp. 119–143]. We show that the estimator of the break fraction has the same convergence rate as obtained in Bai, Lumsdaine and Stock [ Review of Economic Studies (1998 ) Vol. 65, pp. 395–432] and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. However, we also show that our method breaks down when the magnitude of structural change is very small. Simulation experiments reveal how the finite sample distribution approaches the limiting distribution as the magnitude of the break and or the sample size increases. [ABSTRACT FROM AUTHOR]

There exists a clear need for a comprehensive framework for accurately analysing and realistically modelling the key traffic statistics that determine network performance. Recently, a novel traffic model, sinusoid with uniform noise (SUN), has been proposed, which outperforms other models in that it can simultaneously achieve tractability, parsimony, accuracy (in predicting network performance), and efficiency (in real-time capability). In this paper, we design, evaluate and compare several estimation approaches, including variance-based estimation (Var), minimum mean-square-error-based estimation (MMSE), MMSE with the constraint of variance (Var+MMSE), MMSE of autocorrelation function with the constraint of variance (Var+AutoCor+MMSE), and variance of secondary demand-based estimation (Secondary Variance), to determining the key parameters in the SUN model. Integrated with the SUN model, all the proposed methods are able to capture the basic behaviour of the aggregation reservation system and closely approximate the system performance. In addition, we find that: (1) the Var is very simple to operate and provides both upper and lower performance bounds. It can be integrated into other methods to provide very accurate approximation to the aggregation's performance and thus obtain an accurate solution; (2) Var+AutoCor+MMSE is superior to other proposed methods in the accuracy to determine system performance; and (3) Var+MMSE and Var+AutoCor+MMSE differ from the other three methods in that both adopt an experimental analysis method, which helps to improve the prediction accuracy while reducing computation complexity. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In crop protection and ecology accurate and precise estimates of insect populations are required for many purposes. The spatial pattern of the organism sampled, in relation to the sampling scheme adopted, affects the difference between the actual and estimated population density, the bias, and the variability of that estimate, the precision. Field monitoring schemes usually adopt time-efficient sampling regimes involving contiguous units rather than the most efficient for estimation, the completely random sample. This paper uses spatially-explicit ecological field data on aphids and beetles to compare common sampling regimes. The random sample was the most accurate method and often the most precise; of the contiguous schemes the line transect was superior to more compact arrangements such as a square block. Bias depended on the relationship between the size and shape of the group of units comprising the sample and the dominant cluster size underlying the spatial pattern. Existing knowledge of spatial pattern to inform the choice of sampling scheme may provide considerable improvements in accuracy. It is recommended to use line transects longer than the grain of the spatial pattern, where grain is defined as the average dimension of clusters over both patches and gaps, and with length at least twice the dominant cluster size. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of identifying echelon canonical forms for a vector autoregressive moving-average model with exogenous variables using finite algorithms. For given values of the Kronecker indices, a method for estimating the structural parameters of a model using ordinary least squares calculations is presented. These procedures give rise, rather naturally, to a technique for the determination of the structural indices based on the use of conventional model selection criteria. A detailed analysis of the statistical properties of the estimation and identification procedures is given and some evidence on the practical significance of the results obtained is also provided. The conclusion briefly discusses modifications designed to improve the performance of the identification method and points to the application of the techniques to subspace algorithms. [ABSTRACT FROM AUTHOR]