Search Results (26)

Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This becomes particularly pertinent when all models portray similar characteristics within specific subsets of the support of the random variable under scrutiny. Since the actual model is unknown in practical scenarios, the challenge of model selection becomes daunting, complicating the study of associated characteristics of the actual data generation process. To address these challenges, the concept of model averaging is embraced. Often, averaging over multiple models helps alleviate the risk of model misspecification, as different models may capture distinct aspects of the data or modeling assumptions. This enhances the robustness of the estimation process, yielding a more accurate and reasonable estimate compared to relying solely on a single model. This paper introduces two novel data-based model selection methods—one using the likelihood function and the other using the density power divergence measure. The study focuses on estimating the Value-at-Risk (VaR) for non-life insurance claim size data, providing comprehensive insights into potential losses for insurers. The performance of the proposed procedures is evaluated through Monte Carlo simulations under both uncontaminated conditions and in the presence of data contamination. Additionally, the applicability of the methods is illustrated using two real non-life insurance datasets, with the VaR values estimated at different confidence levels. Full article

The Bayesian network is a directed, acyclic graphical model that can offer a structured description for probabilistic dependencies among random variables. As powerful tools for classification tasks, Bayesian classifiers often require computing joint probability distributions, which can be computationally intractable due to potential full dependencies among feature variables. On the other hand, Naïve Bayes, which presumes zero dependencies among features, trades accuracy for efficiency and often comes with underperformance. As a result, non-zero dependency structures, such as trees, are often used as more feasible probabilistic graph approximations; in particular, Tree Augmented Naïve Bayes (TAN) has been demonstrated to outperform Naïve Bayes and has become a popular choice. For applications where a variable is strongly influenced by multiple other features, TAN has been further extended to the k-dependency Bayesian classifier (KDB), where one feature can depend on up to k other features (for a given $k\ge 2$). In such cases, however, the selection of the k parent features for each variable is often made through heuristic search methods (such as sorting), which do not guarantee an optimal approximation of network topology. In this paper, the novel notion of k-tree Augmented Naïve Bayes (k-TAN) is introduced to augment Naïve Bayesian classifiers with k-tree topology as an approximation of Bayesian networks. It is proved that, under the Kullback–Leibler divergence measurement, k-tree topology approximation of Bayesian classifiers loses the minimum information with the topology of a maximum spanning k-tree, where the edge weights of the graph are mutual information between random variables conditional upon the class label. In addition, while in general finding a maximum spanning k-tree is NP-hard for fixed $k\ge 2$, this work shows that the approximation problem can be solved in time $O\left(n^{k+1}\right)$ if the spanning k-tree also desires to retain a given Hamiltonian path in the graph. Therefore, this algorithm can be employed to ensure efficient approximation of Bayesian networks with k-tree augmented Naïve Bayesian classifiers of the guaranteed minimum loss of information. Full article

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches. Full article

Future wireless power transmission will cover power levels up to kilowatts or more and transmission distances up to the scale of kilometers. With its narrow beam divergence angle, optical wireless power transmission (OWPT) is a promising candidate for such system implementations. In the operation of OWPT, it is necessary to estimate the position, direction (azimuth, elevation), and attitude of the target photovoltaic device before the power supply. The authors have proposed the detection of targets using differential absorption imaging and positioning with a combination of stereo imagery. In the positioning by stereo imagery, a condition regarding the consistency of the left and right images can be defined. This corresponds to the certain value of the exposure time of the image sensor, and this depends on the target’s attitude angle. In this paper, we discuss target attitude estimation using this minimum exposure time at which the integrity measure converges. A physical model was derived under general conditions of target position and experimental configuration. Target attitudes were estimated within an error range of 10 to 15 degrees in approximately 60 degrees range. On the other hand, there is an attitude estimation method based on the apparent size of the target. When using this method to estimate the attitude angle, errors are significantly large for specular and diffuse mixed targets like the PV. The method proposed in this paper is a robust attitude estimation method for the photovoltaic device in OWPT. Full article

In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as testing composite null hypotheses, and we provide in this case constrained estimators to inherent restrictions of the underlying distribution. Furthermore, we derive robust Rao-type test statistics based on the MDPDGE for testing a simple null hypothesis, and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation study. Full article

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target. Full article

This paper considers the problem of comparing several means under the one-way Analysis of Variance (ANOVA) setup. In ANOVA, outliers and heavy-tailed error distribution can seriously hinder the treatment effect, leading to false positive or false negative test results. We propose a robust test of ANOVA using an M-estimator based on the density power divergence. Compared with the existing robust and non-robust approaches, the proposed testing procedure is less affected by data contamination and improves the analysis. The asymptotic properties of the proposed test are derived under some regularity conditions. The finite-sample performance of the proposed test is examined via a series of Monte-Carlo experiments and two empirical data examples—bone marrow transplant dataset and glucose level dataset. The results produced by the proposed testing procedure are favorably compared with the classical ANOVA and robust tests based on Huber’s M-estimator and Tukey’s MM-estimator. Full article

Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model information sent from local clients to update the parameters for a global model. Sample mean is the simplest and most commonly used aggregation method. However, it is not robust for data with outliers or under the Byzantine problem, where Byzantine clients send malicious messages to interfere with the learning process. Some robust aggregation methods were introduced in literature including marginal median, geometric median and trimmed-mean. In this article, we propose an alternative robust aggregation method, named $\gamma$-mean, which is the minimum divergence estimation based on a robust density power divergence. This $\gamma$-mean aggregation mitigates the influence of Byzantine clients by assigning fewer weights. This weighting scheme is data-driven and controlled by the $\gamma$ value. Robustness from the viewpoint of the influence function is discussed and some numerical results are presented. Full article

The Rao’s score, Wald and likelihood ratio tests are the most common procedures for testing hypotheses in parametric models. None of the three test statistics is uniformly superior to the other two in relation with the power function, and moreover, they are first-order equivalent and asymptotically optimal. Conversely, these three classical tests present serious robustness problems, as they are based on the maximum likelihood estimator, which is highly non-robust. To overcome this drawback, some test statistics have been introduced in the literature based on robust estimators, such as robust generalized Wald-type and Rao-type tests based on minimum divergence estimators. In this paper, restricted minimum Rényi’s pseudodistance estimators are defined, and their asymptotic distribution and influence function are derived. Further, robust Rao-type and divergence-based tests based on minimum Rényi’s pseudodistance and restricted minimum Rényi’s pseudodistance estimators are considered, and the asymptotic properties of the new families of tests statistics are obtained. Finally, the robustness of the proposed estimators and test statistics is empirically examined through a simulation study, and illustrative applications in real-life data are analyzed. Full article

We detail the development of the ancestry informative single nucleotide polymorphisms (SNPs) panel forming part of the VISAGE Basic Tool (BT), which combines 41 appearance predictive SNPs and 112 ancestry predictive SNPs (three SNPs shared between sets) in one massively parallel sequencing (MPS) multiplex, whereas blood-based age analysis using methylation markers is run in a parallel MPS analysis pipeline. The selection of SNPs for the BT ancestry panel focused on established forensic markers that already have a proven track record of good sequencing performance in MPS, and the overall SNP multiplex scale closely matched that of existing forensic MPS assays. SNPs were chosen to differentiate individuals from the five main continental population groups of Africa, Europe, East Asia, America, and Oceania, extended to include differentiation of individuals from South Asia. From analysis of 1000 Genomes and HGDP-CEPH samples from these six population groups, the BT ancestry panel was shown to have no classification error using the Bayes likelihood calculators of the Snipper online analysis portal. The differentiation power of the component ancestry SNPs of BT was balanced as far as possible to avoid bias in the estimation of co-ancestry proportions in individuals with admixed backgrounds. The balancing process led to very similar cumulative population-specific divergence values for Africa, Europe, America, and Oceania, with East Asia being slightly below average, and South Asia an outlier from the other groups. Comparisons were made of the African, European, and Native American estimated co-ancestry proportions in the six admixed 1000 Genomes populations, using the BT ancestry panel SNPs and 572,000 Affymetrix Human Origins array SNPs. Very similar co-ancestry proportions were observed down to a minimum value of 10%, below which, low-level co-ancestry was not always reliably detected by BT SNPs. The Snipper analysis portal provides a comprehensive population dataset for the BT ancestry panel SNPs, comprising a 520-sample standardised reference dataset; 3445 additional samples from 1000 Genomes, HGDP-CEPH, Simons Foundation and Estonian Biocentre genome diversity projects; and 167 samples of six populations from in-house genotyping of individuals from Middle East, North and East African regions complementing those of the sampling regimes of the other diversity projects. Full article

When performing AC/DC-DC/AC power conversions, multilevel converters provide several advantages as compared to classical two-level converters. This paper deals with the dynamic modeling, control, and robustness assessment of multilevel flying-capacitor converters. The dynamic model is derived using the Power-Oriented Graphs modeling technique, which provides the user with block schemes that are directly implementable in the Matlab/Simulink environment by employing standard Simulink libraries. The performed robustness assessment has led to the proposal of a divergence index, which allows for evaluating the voltage balancing capability of the converter using different voltage vector configurations for the extended operation of the converter, namely when the number of output voltage levels is increased for a given number of capacitors. A new variable-step control algorithm is then proposed. The variable-step control algorithm safely enables the converter extended operation, which prevents voltage balancing issues, even under particularly unfavorable conditions, such as a constant desired output voltage or a sudden load change. The simulation results showing the good performances of the proposed variable-step control as compared to a classical minimum distance approach are finally provided and commented in detail. Full article

Tomatoes are usually consumed daily in the human diet. High temperatures reduce the number of tomato yields per year. Heat stress has been considered one of the most prominent causes of alterations in morphological and molecular characteristics in crops that decrease normal growth, production, and yield in diverse plants, including tomatoes (Solanum lycopersicum L.). In this study, we evaluated six tomato lines, namely G1, G2, G3, G4, G5, and G6, at morphological, molecular, and cytological levels under heat stress. The average results of two seasons (2018 and 2019) clarified that the G6, G1, and G2 lines recorded the highest flowering values, as well as some fruit and vegetative growth traits. Furthermore, G6 and G2 had the maximum number of fruits/plant, whereas G2 and G1 produced the highest yield/plant under high temperatures. The number of chromosomes in all lines was 2n = 24, except for G5, in which the number was 2n = 26, whereas chromosome sizes were small, ranging from 323.08 to 464.48 µm. The G1 cultivar was a symmetrical cultivar (primitive), having the highest total form percentage (TF%) and symmetry index (Syi) values and the minimum karyotype asymmetry index (ASK) value, whereas G4 was asymmetrical (advanced). Molecular marker analysis demonstrated that intersimple sequence repeat (ISSR) primers 49A, HB-14, 49A, 49B, and 89B presented the highest values for polymorphism percentage P%, marker index (MI), effective multiplex ratio (EMR), and polymorphism information content (PIC), respectively. In contrast, OP-A3, OP-B3, SCoT 2, and SCoT 12 primers showed the highest PIC, EMR, MI, P%, and resolving power (Rp) values across the studied random amplified polymorphic DNA (RAPD) and start codon-targeted (SCoT) primers. Moreover, ISSR revealed the highest number of unique specific markers (6), followed by RAPD (4) and SCoT (3) markers. Cluster analysis of combined cytological data and data relating to molecular marker attributes separated the G1, G2, and G3 lines into one group, whereas the other lines were clustered in another group. On the whole, the application of combined analysis using morphological, cytological, and molecular genetics techniques could be considered to provide suitable parameters for studying the evolution of the genetic divergence between the studied tomato lines. Full article

In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of outliers. Finally, a real data analysis using monthly count series of crimes in New South Wales and an artificial data example are provided as an illustration. Full article

The distance constrained maximum likelihood procedure (DCML) optimally combines a robust estimator with the maximum likelihood estimator with the purpose of improving its small sample efficiency while preserving a good robustness level. It has been published for the linear model and is now extended to the GLM. Monte Carlo experiments are used to explore the performance of this extension in the Poisson regression case. Several published robust candidates for the DCML are compared; the modified conditional maximum likelihood estimator starting with a very robust minimum density power divergence estimator is selected as the best candidate. It is shown empirically that the DCML remarkably improves its small sample efficiency without loss of robustness. An example using real hospital length of stay data fitted by the negative binomial regression model is discussed. Full article

In this study, we consider an online monitoring procedure to detect a parameter change for integer-valued generalized autoregressive heteroscedastic (INGARCH) models whose conditional density of present observations over past information follows one parameter exponential family distributions. For this purpose, we use the cumulative sum (CUSUM) of score functions deduced from the objective functions, constructed for the minimum power divergence estimator (MDPDE) that includes the maximum likelihood estimator (MLE), to diminish the influence of outliers. It is well-known that compared to the MLE, the MDPDE is robust against outliers with little loss of efficiency. This robustness property is properly inherited by the proposed monitoring procedure. A simulation study and real data analysis are conducted to affirm the validity of our method. Full article