MYStIX (Massive Young Star-Forming Complex Study in Infrared and Xray) seeks to characterize 20 OB-dominated young clusters and their environs at distances d ≤ 4 kpc using imaging detectors on the Chandra X-ray Observatory, Spitzer Space Telescope, and the United Kingdom InfraRed Telescope. The observational goals are to construct catalogs of star-forming complex stellar members with well-defined criteria, and maps of nebular gas (particularly of * We describe here the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX) that combines the virtues of multiwavelength selection of young stellar populations − optical band for OB stars, infrared bands for young stars with protoplanetary disks, and the X-ray band for OB stars and pre-main sequence flaring stars. Uniform analysis procedures are applied wherever possible to the targeted MSFRs. Effort is exerted to obtain high-sensitivity catalogs from the X-ray and infrared images using advanced algorithms designed to treat the nebular and crowding problems. Probabilistic catalog matching and -5source classification algorithms give objective selection of cluster members; incompleteness and selection biases are still present but are reduced to acceptable levels for many purposes.
In the statistical analysis of spatial point patterns, it is often important to investigate whether the point pattern depends on spatial covariates. This paper describes nonparametric (kernel and local likelihood) methods for estimating the effect of spatial covariates on the point process intensity. Variance estimates and confidence intervals are provided in the case of a Poisson point process. Techniques are demonstrated with simulated examples and with applications to exploration geology and forest ecology.
Existing methods of partitioning the market index into bull and bear regimes do not identify market corrections or bear market rallies. In contrast, our probabilistic model of the return distribution allows for rich and heterogeneous intra-regime dynamics. We focus on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. We show how to compute the predictive density of long-horizon returns and discuss the improvements our model provides over benchmarks.
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