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?-measure for continuous state branching processes and its application
Weijuan CHU, Yan-Xia REN
Front Math Chin. 2011, 6 (6): 1045-1058.
https://doi.org/10.1007/s11464-011-0122-0
In this paper, we first give a direct construction of the ?-measure of a continuous state branching process. Then we prove, with the help of this ?-measure, that any continuous state branching process with immigration can be constructed as the independent sum of a continuous state branching process (without immigration), and two immigration parts (jump immigration and continuum immigration). As an application of this construction of a continuous state branching process with immigration, we give a proof of a necessary and sufficient condition, first stated without proof by M. A. Pinsky [Bull. Amer. Math. Soc., 1972, 78: 242-244], for a continuous state branching process with immigration to a proper almost sure limit. As another application of the ?-measure, we give a “conceptual” proof of an Llog L criterion for a continuous state branching process without immigration to have an L1-limit first proved by D. R. Grey [J. Appl. Prob., 1974, 11: 669-677].
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Tolerance interval for exponential distribution
Jiong DU, Xiangzhong FANG
Front Math Chin. 2011, 6 (6): 1059-1066.
https://doi.org/10.1007/s11464-011-0117-x
Tolerance interval is a kind of interval that assures the probability of at least a given proportion of population falls into the interval attains to a fixed level. It is widely needed in various industrial practices and business activities, such as product design, reliability analysis, and quality inspection. However, comparing to its widely needs, the research on it is still quite limited. In this paper, we propose a numerical method to compute the tolerance interval for exponential distribution. As the simulation study illustrates, our method performs consistently well as the sample size varies. In particular, its good performance for small sample endows itself broadly potential usefulness in practice.
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Optimal control of a big financial company with debt liability under bankrupt probability constraints
Zongxia LIANG, Bin SUN
Front Math Chin. 2011, 6 (6): 1095-1130.
https://doi.org/10.1007/s11464-011-0120-2
This paper considers an optimal control of a big financial company with debt liability under bankrupt probability constraints. The company, which faces constant liability payments and has choices to choose various production/business policies from an available set of control policies with different expected profits and risks, controls the business policy and dividend payout process to maximize the expected present value of the dividends until the time of bankruptcy. However, if the dividend payout barrier is too low to be acceptable, it may result in the company’s bankruptcy soon. In order to protect the shareholders’ profits, the managements of the company impose a reasonable and normal constraint on their dividend strategy, that is, the bankrupt probability associated with the optimal dividend payout barrier should be smaller than a given risk level within a fixed time horizon. This paper aims at working out the optimal control policy as well as optimal return function for the company under bankrupt probability constraint by stochastic analysis, partial differential equation and variational inequality approach. Moreover, we establish a riskbased capital standard to ensure the capital requirement can cover the total given risk by numerical analysis, and give reasonable economic interpretation for the results.
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Generate gene expression profile from high-throughput sequencing data
Hui LIU, Zhichao JIANG, Xiangzhong FANG, Hanjiang FU, Xiaofei ZHENG, Lei CHA, Wuju LI
Front Math Chin. 2011, 6 (6): 1131-1145.
https://doi.org/10.1007/s11464-011-0123-z
This work presents two methods, the Least-square and Bayesian method, to solve the multiple mapping problem in extracting gene expression profiles through the next-generation sequencing. We parallel the tag sequences to genome, and partition them to improving the methods’ efficiency. The essential feature of these methods is that they can solve the multiple mapping problem between genes and short-reads, while generating almost the same estimation in single-mapping situation as the traditional approaches. These two methods are compared by simulation and a real example, which was generated from radiation-induced lung cancer cells (A549), through mapping short-reads to human ncRNA database. The results show that the Bayesian method, as realized by Gibbs sampler, is more efficient and robust than the Least-square method.
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An ergodic theorem of a parabolic Anderson model driven by Lévy noise
Yong LIU, Jianglun WU, Fengxia YANG, Jianliang ZHAI
Front Math Chin. 2011, 6 (6): 1147-1183.
https://doi.org/10.1007/s11464-011-0124-y
In this paper, we study an ergodic theorem of a parabolic Andersen model driven by Lévy noise. Under the assumption that A=(a(i,j))i,j∈S is symmetric with respect to a σ-finite measure π, we obtain the long-time convergence to an invariant probability measure νh starting from a bounded nonnegative A-harmonic function h based on self-duality property. Furthermore, under some mild conditions, we obtain the one to one correspondence between the bounded nonnegative A-harmonic functions and the extremal invariant probability measures with finite second moment of the nonnegative solution of the parabolic Anderson model driven by Lévy noise, which is an extension of the result of Y. Liu and F. X. Yang.
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A study of biases of DNA copy number estimation based on PICR model
Quan WANG, Jianghan QU, Xiaoxing CHENG, Yongjian KANG, Lin WAN, Minping QIAN, Minghua DENG
Front Math Chin. 2011, 6 (6): 1203-1216.
https://doi.org/10.1007/s11464-011-0125-x
Affymetrix single-nucleotide polymorphism (SNP) arrays have been widely used for SNP genotype calling and copy number variation (CNV) studies, both of which are dependent on accurate DNA copy number estimation significantly. However, the methods for copy number estimation may suffer from kinds of difficulties: probe dependent binding affinity, crosshybridization of probes, and the whole genome amplification (WGA) of DNA sequences. The probe intensity composite representation (PICR) model, one former established approach, can cope with most complexities and achieve high accuracy in SNP genotyping. Nevertheless, the copy numbers estimated by PICR model still show array and site dependent biases for CNV studies. In this paper, we propose a procedure to adjust the biases and then make CNV inference based on both PICR model and our method. The comparison indicates that our correction of copy numbers is necessary for CNV studies.
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14 articles
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