962 research outputs found

    Some Aspects on Data Modelling

    Get PDF
    Statistical methods are motivated by the desire of learning from data. Transaction dataset and time-ordered data sequence are commonly found in many research areas, such as finance, bioinformatics and text mining. In this dissertation, two problems regarding these two types of data: association rule mining from transaction data and structural change estimation in time-ordered sequence, are studied. Informative association rule mining is fundamental for knowledge discovery from transaction data, for which brute-force search algorithms, e.g., the well-known Apriori algorithm, were developed. However, operating these algorithms becomes computationally intractable in searching large rule space. A stochastic search framework is developed to tackle this challenge by imposing a probability distribution on the association rule space and using the idea of annealing Gibbs sampling. Large rule space of exponential order can still be randomly searched by this algorithm to generate a Markov chain of viable length. This chain contains the most informative rules with probability one. The stochastic search algorithm is flexible to incorporate any measure of interest. Moreover, it reduces computational complexities and large memory requirements. A time-ordered data sequence may contain some sudden changes at some time points, before and after which the data sequences follow different distributions or statistical models. Change point problems in generalized linear models and distributions of independent random variables are studied respectively. Firstly, to estimate multiple change points in generalized linear models, we convert it into a model selection problem. Then modern model selection techniques are applied to estimate the regression coefficients. A consistent estimator of the number of change points is developed, and an algorithm is provided to estimate the change points. Secondly, to estimate single change point in distributions of independent random variables, a change point estimator is proposed based on empirical characteristic functions. Its consistency is also established

    How do sub-national institutional constraints impact foreign firm performance?

    Get PDF
    This paper examines the impact of sub-national institutions on the performance of foreign firms in China. Building on institutional theory, we envisage that the negative effect of sub-national institutional constraints is moderated by firm size and age, entry mode, and market orientation. Our hypotheses are tested on a large-firm-level dataset of about 29,000 foreign firms in 120 cities in China within the period of 1999–2005. We find that firm size and age both have a diminishing positive impact on foreign firm performance; moreover, there is a U-shaped relationship between firm age and foreign firm performance in cities with higher level institutional constraints. We also find that joint ventures help mitigate the negative impact of sub-national institutional constraints on foreign firm performance when the level of institutional constraints is higher

    A free boundary tumor model with time dependent nutritional supply

    Get PDF
    A non-autonomous free boundary model for tumor growth is studied. The model consists of a nonlinear reaction diffusion equation describing the distribution of vital nutrients in the tumor and a nonlinear integro-differential equation describing the evolution of the tumor size. First the global existence and uniqueness of a transient solution is established under some general conditions. Then with additional regularity assumptions on the consumption and proliferation rates, the existence and uniqueness of steady-state solutions is obtained. Furthermore the convergence of the transient solutions toward the steady-state solution is verified. Finally the long time behavior of the solutions is investigated by transforming the time-dependent domain to a fixed domain.Ministerio de Economía y Competitividad (MINECO). EspañaEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Junta de AndalucíaNational Natural Science Foundation of ChinaSimons Foundatio

    A Novel Multiobjective Optimization Method Based on Sensitivity Analysis

    Get PDF
    For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm) with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient). In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method

    Magnetic Resonance Characterization of Ischemic Tissue Metabolism

    Get PDF
    Magnetic resonance imaging (MRI) and spectroscopy (MRS) are versatile diagnostic techniques capable of characterizing the complex stroke pathophysiology, and hold great promise for guiding stroke treatment. Particularly, tissue viability and salvageability are closely associated with its metabolic status. Upon ischemia, ischemic tissue metabolism is disrupted including altered metabolism of glucose and oxygen, elevated lactate production/accumulation, tissue acidification and eventually, adenosine triphosphate (ATP) depletion and energy failure. Whereas metabolism impairment during ischemic stroke is complex, it may be monitored non-invasively with magnetic resonance (MR)-based techniques. Our current article provides a concise overview of stroke pathology, conventional and emerging imaging and spectroscopy techniques, and data analysis tools for characterizing ischemic tissue damage

    Comparison of Adjunctive Naoxintong versus Clopidogrel in Volunteers with the CYP2C19*2 Gene Mutation Accompanied with Qi Deficiency and Blood Stasis Constitution

    Get PDF
    This study was to determine the impact of adjunctive Buchang Naoxintong Jiaonang (BNJ) to clopidogrel on volunteers with the CYP2C19*2 gene mutation accompanied with qi deficiency and blood stasis (QDBS) constitution. Eighteen males with QDBS constitution were selected, who were 6 CYP2C19*1/*1, 6 CYP2C19*1/*2, and 6 CYP2C19*2/*2, and signed informed consent. Results showed that the maximal platelet aggregation (Aggmax) and 5 min aggregation (Agglate) with 5-μmol/L ADP in three different CYP2C19*2 genotypes were significantly decreased after any drug therapy compared with corresponding baseline measurements (all values P < .05). But percent inhibitions of Aggmax and Agglate (IPAs) in CYP2C19*2/*2 genotype at 4 hours, 24 hours, 3 days, and 7 days after clopidogrel administration were all the lowest among three CYP2C19*2 genotypes (P < .01), and IPAs in CYP2C19*1/*2 genotype were between CYP2C19*1/*1 and CYP2C19*2/*2. IPAs had no significant difference among three different CYP2C19*2 genotypes after BNJ or adjunctive BNJ. In addition, changes of CD62P, PAC1, and sCD40L were similar to changes of ADP-induced platelet aggregation in three different CYP2C19*2 genotypes. Conclusion was that adjunctive BNJ to clopidogrel can enhance the antiplatelet effect in volunteers with the CYP2C19*2 gene mutation
    corecore