Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong

Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong [arXiv:1104.3073]Comment: Published in at http://dx.doi.org/10.1214/11-STS345C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Generic definitions and species assignments in the family Epipsocidae (Psocoptera)

The family Epipsocidae is defined, with the addition of one character not previously used. The genus Dimidistriata Li and Mockford is removed from Epipsocidae and tentatively placed in the family Dolabellopsocidae. The genus Parepipsocus Badonnel remains unplaceable to family. Eleven genera are recognized within Epipsocidae. Bertkauia Kolbe, which had been synonymized with Epipsocus, is recognized as a valid genus. Definitions of genera based on the type species and seemingly close relatives result in 38 new combinations. Epipsocus Hagen, which previously held most of the species, now serves a dual function, with 15 species in the strict sense and 15 species 'incertae sedis' retained in it, pending further investigation. Goja Navas, previously with 2 species, now contains 10, with 8 transferred from Epipsoc us. Mesepipsocus Badonnel, previously with 5 species, receives 24 more from Epipsocus and 1 from Dicropsocus. Eight species are transferred from Epipsocus to Epipsocopsis Badonnel. Notable consistency in geographic distributions of genera results from these transfers. Some evolutionary trends within the family are discussed. Epipsocus delicatus (Hagen) and E. completus Banks, which had been assigned by recent authors to the genus Pseudocaecilius Enderlein (Family Pseudocaeciliidae), are returned to the Epipsocidae and assigned to Epipsocopsis. A lectotype is designated for E. delicatus

Dynamic Bayesian Nonlinear Calibration

Statistical calibration where the curve is nonlinear is important in many areas, such as analytical chemistry and radiometry. Especially in radiometry, instrument characteristics change over time, thus calibration is a process that must be conducted as long as the instrument is in use. We propose a dynamic Bayesian method to perform calibration in the presence of a curvilinear relationship between the reference measurements and the response variable. The dynamic calibration approach adequately derives time dependent calibration distributions in the presence of drifting regression parameters. The method is applied to microwave radiometer data and simulated spectroscopy data based on work by Lundberg and de Mar\'{e} (1980)