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diverse cloud applications deployed on-demand make for workload burstiness. Burstiness is quantified statistically through different variance measures. This paper focuses on the statistical measures used to quantify cloud workload burstiness. Using diverse workloads, it identifies different statistical models that uniquely capture workload specific burstiness. Subsequently, it employs recent econometric...
In short observational records, with reducing the data length it becomes increasingly difficult to distinguish between long-term and short-term memory based on the analysis of single data series. Here we investigate the applicability of autoregressive modelling to long-term correlated data. The applicability of AR model is shown in terms of the autocorrelation function and power spectral density.
Based on related statistical data about Chinese cultural consumption, this paper analyzes and compares the regional differences of it in China in the environment, level and tendency of cultural consumption by establishing statistical models, and explores the main influencing factors in different regions. Finally this paper puts forward some policies and suggestions according to the results to elevate...
The study of patients in Intensive Care Units (ICUs) is a crucial task in critical care research which has significant implications both in identifying clinical risk factors and defining institutional guidances. The mortality study of ICU patients is of particular interest because it provides useful indications to healthcare institutions for improving patients experience, internal policies, and procedures...
In this paper, the problem of identifying correlated components in a high-dimensional Gaussian vector is considered. In the setup considered, instead of having to take a full-vector observation at each time index, the observer is allowed to observe any subset or full set of components in the vector, and he has the freedom to design his sampling strategies over time. The observer aims to find an optimal...
This paper deals with model comparison based on the Jeffrey's divergence (JD). More particularly, after providing the JD between the joint distributions of k consecutive values of a white noise and the ones of a real moving-average or autoregressive model, the JD between real 1st-order MA and real 1st-order AR models is studied. Except when the 1st MA parameter is equal to 1, we show that, after a...
When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes thespatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data,...
Modern Russian management model is a mixture management methods based on post-Soviet economic reality and Western best practices. Entering the global economy in the 90th Russia was forced to adopt foreign management models suitable for new market reality. Nevertheless foreign management models could not be copied in pure forms. The authors conducted the analysis of the Generation theory (baby boomers,...
This paper proposes a state space model to describe multivariate autocorrelated zero-inflated count series. The model extends the classical zero-inflated Poisson distribution into multivariate cases but is able to impose different zero inflations on different dimensions. Combing the zero inflation with the log-normal mixture of independent Poisson distribution, this model allows for flexible cross-correlations...
Each and every company are interested to know about the status of performance in their business. Bankruptcy prediction is an important issue and it plays a vital role in creating decisions in the field of corporate and financial organization. It has its influence on both economic as well as social factors, e.g., investors, creditors, government, managers, employees, etc. So, it is important and useful...
We introduce a novel dynamic model for discrete time-series data, in which the temporal sampling may be nonuniform. The model is specified by constructing a hierarchy of Poisson factor analysis blocks, one for the transitions between latent states and the other for the emissions between latent states and observations. Latent variables are binary and linked to Poisson factor analysis via Bernoulli-Poisson...
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster?...
In order to evaluate the energy production of a solar system, the tilted global radiation is needed. Generally, only the global horizontal radiation data are available. To calculate a tilted global radiation, it is necessary to estimate the diffuse or the direct component of the horizontal solar radiation. In this article, a statistical procedure has been employed to develop correlations between the...
We presented a multi-scale model, by coupling of three different component models: individual based model (IBM), spatial moment-based model (SMBM) and equation-based model (EBM), for studying of spreading of infectious disease in a 3D office building. We proposed an up-scaling methodology to derive a SMBM from a given IBM, and to generate an EBM from the SMBM. Our results showed that the multi-scale...
One of the main aims of systems biology is understanding of the biological system as a whole rather than focusing on individual factors, which can be achieved by data integration from different experiments allowing deeper insights in to the mechanisms of complex traits. With the rise of next-generation sequencing technologies and through large-scale consortia projects massive amount of heterogeneous...
Based on existing grey relational degree models, in order to investigate the dynamic similarity of trends between sequences, we propose the grey relational model based on velocity and acceleration which measures the closeness of the rate of change. And then we discuss the nature of the rate of change of gray relational degree. This model can reflect the similarity of the relative change trend of time...
Recently, there are many big data analytic models which have been developed. In this paper, we consider the spatial panel model and extend the STIRPAT model. These two models are integrated for us to investigate the effects of the urbanization on energy consumption in China. Our study is based on the panel data of 29 provinces in China from 2002 to 2013. Several conclusions are made from our investigation...
The optimal experimental design (OED) for observation strategy is investigated in this paper to collect the most informative experimental data for parameter estimation. The aim is to determine the best sampling time points and also select the most valuable measurement state variables through OED. The two design objectives are integrated together as a single-objective optimisation problem in which...
For patients with large or recurrent laryngeal cancer, total laryngectomy surgery is usually performed. In the absence of a larynx, tracheoesophageal (TE) voice is the gold standard for alaryngeal phonation. The mechanisms underlying TE voice are unknown, but pharyngoesophageal (PE) segment vibration has been shown to play a key role. The aim of this study was to systematically explore the mechanics...
Most current Brain-Computer Interfaces (BCIs) achieve high information transfer rates using spelling paradigms based on stimulus-evoked potentials. Despite the success of this interfaces, this mode of communication can be cumbersome and unnatural. Direct synthesis of speech from neural activity represents a more natural mode of communication that would enable users to convey verbal messages in real-time...
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