At the specific power of electron cyclotron resonance (ECR) heating of 3.2 MW m(-3) (plasma density of 2 x 10(19) m(-3), electron temperature of 0.6 keV), an increase in the plasma energy lifetime by not less than 30%...
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At the specific power of electron cyclotron resonance (ECR) heating of 3.2 MW m(-3) (plasma density of 2 x 10(19) m(-3), electron temperature of 0.6 keV), an increase in the plasma energy lifetime by not less than 30% is accompanied by a two-time-decrease in the level of short-wave turbulent density fluctuations. In such a shot, before the beginning of the quasi-stationary confinement stage, the turbulent state of density fluctuations is characterized by the stronger deviation from zero of the coefficient of excess of fluctuation increments than it is in shots without transport transitions. This indicates the stronger deviation of the probability distribution function of density fluctuation increments from the normal law in shots with transport transitions. Based on the analysis of increments of short-wave fluctuations using the special method for separating the continuous components in stochastic processes, a qualitative difference was established between the behaviors of the structural components forming the plasma turbulence in shots with and without transport transitions. In addition, for shots with transport transitions, a change in the shape of the approximating finite mixture of normal distributions and parameters of its component densities is demonstrated.
Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the ...
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Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust em algorithm to estimate the prior probabilities of being null and the distribution of p-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.
A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise cons...
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A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the expectation maximisation (em) algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature in the em algorithm, a L-0 penalised likelihood method is implemented in order to automatically determine the number and locations of the cuts of the baseline hazard. This procedure allows to detect specific areas of time where patients are at greater risks for ankylosis. The method can be directly extended to the inclusion of exact observations and to a cure fraction. Theoretical results are obtained which allow to derive statistical inference of the model parameters from asymptotic likelihood theory. Through simulation studies, the penalisation technique is shown to provide a good fit of the baseline hazard and precise estimations of the resulting regression parameters.
Independence and normality of observations for each level of the classification variables are two fundamental assumptions in traditional analysis of variance (ANOVA), whereas in many real applications the data violate...
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Independence and normality of observations for each level of the classification variables are two fundamental assumptions in traditional analysis of variance (ANOVA), whereas in many real applications the data violate seriously from these assumptions. Accordingly, in these situations using this traditional theory leads to unappealing results. We consider time series ANOVA by assuming a skew normal distribution for innovations. We provide iterative closed forms for the maximum likelihood estimators and construct asymptotic confidence intervals for them. A simulation study and a real data example are used to evaluate the efficiency and applicability of the proposed model for analyzing skew-symmetric time series data.
In this paper we study a finite Gaussian mixture model with an additional uniform component that has the role to catch points in the tails of the data distribution. An adaptive constraint enforces a certain level of s...
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In this paper we study a finite Gaussian mixture model with an additional uniform component that has the role to catch points in the tails of the data distribution. An adaptive constraint enforces a certain level of separation between the Gaussian mixture components and the uniform component representing noise and outliers in the tail of the distribution. The latter makes the proposed tool particularly useful for robust estimation and outlier identification. A constrained ML estimator is introduced for which existence and consistency is shown. One of the attractive features of the methodology is that the noise level is estimated from data. We also develop an em-type algorithm with proven convergence. Based on numerical evidence we show how the methods developed in this paper are useful for several fundamental data analysis tasks: outlier identification, robust location-scale estimation, clustering, and density estimation.
The fact that a large proportion of insurance policyholders make no claims during a one-year period highlights the importance of zero-inflated count models when analyzing the frequency of insurance claims. There is a ...
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The fact that a large proportion of insurance policyholders make no claims during a one-year period highlights the importance of zero-inflated count models when analyzing the frequency of insurance claims. There is a vast literature focused on the univariate case of zero-inflated count models, while work in the area of multivariate models is considerably less advanced. Given that insurance companies write multiple lines of insurance business, where the claim counts on these lines of business are often correlated, there is a strong incentive to analyze multivariate claim count models. Motivated by the idea of Liu and Tian (Computational Statistics and Data Analysis, 83, 200-222;2015), we develop a multivariate zero-inflated hurdle model to describe multivariate count data with extra zeros. This generalization offers more flexibility in modeling the behavior of individual claim counts while also incorporating a correlation structure between claim counts for different lines of insurance business. We develop an application of the expectation-maximization (em) algorithm to enable the statistical inference necessary to estimate the parameters associated with our model. Our model is then applied to an automobile insurance portfolio from a major insurance company in Spain. We demonstrate that the model performance for the multivariate zero-inflated hurdle model is superior when compared to several alternatives.
This paper extends the multivariate skew t distributions with independent logistic skewing functions (MSTIL) introduced in Kwong and Nadarajah (Methodology and Computing in Applied Probability 24 (2022) 1669-1691) to ...
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This paper extends the multivariate skew t distributions with independent logistic skewing functions (MSTIL) introduced in Kwong and Nadarajah (Methodology and Computing in Applied Probability 24 (2022) 1669-1691) to finite mixture models (FM-MSTIL). A stochastic em-type algorithm is proposed for fitting the FM-MSTIL, and a divisive hierarchical algorithm is proposed for initialisations and model selections. We show that the model can outperform other finite mixture models in the literature for some simulated data sets. The performance of the FM-MSTIL in cluster analysis is also investigated. We show that the FM-MSTIL-R, a nested version of the FM-MSTIL, performs well for automatic gating tasks on some flow cytometry data sets in the FlowCap-I challenge. The FM-MSTIL-R achieved a better overall score than all other competing algorithms in the original challenge. An efficient implementation of the FM-MSTIL is available as an R package in GitHub.
The first step in process mining of a business is to collect data on the process that is being analyzed. Sometimes the response variable in a longitudinal study of customers is a count variable. It may seem that the e...
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Evaluation of the effectiveness of various clustering methods using an enhanced K-Mean algorithm is shown in this paper. Essential data mining techniques, such as clustering techniques, divide things into proper subgr...
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Markov switching models are widely used in the time series field for their ability to describe the impact of latent regimes on the behaviour of response variables. Meanwhile, Markov switching quantile regression model...
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Markov switching models are widely used in the time series field for their ability to describe the impact of latent regimes on the behaviour of response variables. Meanwhile, Markov switching quantile regression models with fixed transition probabilities (MSQR-FTP) also provide rich dynamics to modeling financial data, however, it is not always clear how to describe the dynamics on the transition probabilities. This paper extends the transition probabilities to be the time-varying case by allowing them to include information from related variables. By establishing a connection between a quantile regression and an asymmetric Laplace distribution, this paper proposes a maximum likelihood estimation (MLE) method for MSQR-TVTP, and shows the consistency of the MLE. Finally, the performance of the proposed method is illustrated through a simulation study. As an empirical application, we further apply the method to the S&P 500 weekly percentage returns.
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