PHASE-TYPE DISTRIBUTIONS FOR CLAIM SEVERITY REGRESSION MODELING

This paper addresses the task of modeling severity losses using segmentation when the data distribution does not fall into the usual regression frameworks. This situation is not uncommon in lines of business such as third-party liability insurance, where heavy-tails and multimodality often hamper a direct statistical analysis. We propose to use regression models based on phase-type distributions, regressing on their underlying inhomogeneous Markov intensity and using an extension of the expectation–maximization algorithm. These models are interpretable and tractable in terms of multistate processes and generalize the proportional hazards specification when the dimension of the state space is larger than 1. We show that the combination of matrix parameters, inhomogeneity transforms, and covariate information provides flexible regression models that effectively capture the entire distribution of loss severities.

Estimation and Analysis of Sports Energy Consumption Based on Gait Tactile Parameters

The morbidity of obesity and related metabolic syndrome is on the rise, which may be related to the decrease of physical activity. Therefore, keeping energy balance is the basic premise to prevent multiple metabolic syndromes, and the research on the composition and application of energy consumption has become a hot spot. The combination of expectation-maximization algorithm and MapReduce computing model realizes the migration of traditional algorithm to “cloud computing” platform. The physical fitness evaluation algorithm based on collaborative filtering is constructed, and a gait tactile recognition algorithm is proposed by feature selection based on the MEMS sensor. Finally, the algorithm is tested, and a conclusion is drawn. This algorithm is effective in monitoring and recognizing human gait. With the increase of weightlessness characteristics, the sensitivity of detection remains unchanged, and the specificity will increase. In a word, it is scientific and effective. Thus, the reference for establishing the index system of tactile biomechanical parameters of adolescent gait and studying the low-cost and portable energy measurement method of multiparameter indexes is provided.

Real-time user clickstream behavior analysis based on apache storm streaming

This paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior. User-generated clickstream is first stored in a client site browser. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains. We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.

PROBABILISTIC ANALYSIS OF THE MAXIMAL LOAD OF INDUSTRIAL MACHINES

This study describes the statistical analysis of peak forces of industrial washing machines. The data source comes from twelve different machines. The measurements are done using a force gauge installed in places for fastening screws. A new software based on LabView has been developed to gauge the acting forces. To determine extreme force values, various probability distributions are applied. Furthermore, a convex combination of lognormal distribution is used in more complicated cases. The parameters of the lognormal mixtures are determined using modified an Expectation-maximization algorithm. Finally, the achieved results are interpreted with regard to the engineering design and to the operating reliability.

Fast γ Photon Imaging for Inner Surface Defects Detecting

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.