large number of clusters to reduce the sensitivity to initialization, and determines the actual number of clusters by a process of competitive agglomeration. Noise immunity is achieved by incorporating concepts from robust statistics into the algorithm. RCA assigns two different sets of weights for each

the character of the point-process generation mechanism. In the context of point-process simulation, reduction of this discrepancy requires generating data sets with either a large number of points, or with low jitter in the generation of the points. In the context of fractal data analysis, the results

our analysis of these non-experimental data as an important complement to experimental sources and a first step towards establishing whether Head Start confers long-term benefits on participants. For reasons spelled out in detail below, our methods likely provide lower bound estimates of any positive

Abstract not found

estimators of F; G and K based on the analogy with censored survival data: the distance from a fixed point to the nearest point of the process is right-censored by its distance to the boundary of the window. The resulting estimators have a ratio-unbiasedness property that is standard in spatial statistics

We analyze the consumption-portfolio selection problem of an investor facing both Brownian and jump risks. By adopting a factor structure for the asset returns and decomposing the two types of risks on a well chosen basis, we provide a new methodology for determining the optimal solution up to an implicitly defined constant, which in some cases can be reduced to a fully explicit closed form, irrespectively of the number of assets available to the investor. We show that the optimal policy is for the investor to focus on controlling his exposure to the jump risk, while exploiting differences in the asset returns diffusive characteristics in the orthogonal space. We also examine the solution to the portfolio problem as the number of assets increases and the impact of the jumps on the diversification of the optimal portfolio.

Abstract. Recently many scientific and engineering applications have involved the challenging task of analyzing large amounts of unsorted high-dimensional data that have very complicated structures. From both geometric and statistical points of view, such unsorted data are considered mixed

simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a

they rely on searching neighbors among large sets of d-dimensional vectors. This computational burden can be reduced by pre-structuring the data, e.g. using binary trees as proposed by the Approximated Nearest Neighbor (ANN) library. Yet, the recent opening of Graphics Processing Units (GPU

Abstract not found