The effects of estimation of censoring, truncation, transformation and partial data vectors

The purpose of this research was to attack statistical problems concerning the estimation of distributions for purposes of predicting and measuring assembly performance as it appears in biological and physical situations. Various statistical procedures were proposed to attack problems of this sort, that is, to produce the statistical distributions of the outcomes of biological and physical situations which, employ characteristics measured on constituent parts. The techniques are described

Confidence regions for variance ratios in variance components models

Statistical mechanics for constructing confidence intervals for variance ratios in balanced and unbalanced experimental design

Archeological Survey of the South Tyger Watershed

At the request of the Soil Conservation Service an archeological survey was conducted by the Institute of Archeology and Anthropology, University of South Carolina on three proposed reservoir projects in the South Tyger River Watershed, Greenville County, South Carolina. The survey took place in February 1975 and revealed eight previously unrecorded sites in the watershed area. None of these sites are of a significance to warrant further extensive investigation, and there is little possibility that the reservoir projects will destroy any significant archeological sites.https://scholarcommons.sc.edu/archanth_books/1058/thumbnail.jp

Postcard: 1st Annual Chrysanthemum Show, Nov. 9, \u2707. Pres. Guild, Argonia, Kansas

This black and white photographic postcard features rows of Chrysanthemums in vases. Three rows of vases are on shelves covered with cloth. There is written text on the bottom of the card. Handwriting is on the back of the card.https://scholars.fhsu.edu/tj_postcards/1519/thumbnail.jp

The Ashley River: A Survey of Seventeenth Century Sites

https://scholarcommons.sc.edu/archanth_books/1183/thumbnail.jp

Stochastic compartmental analysis - Some applications and examples of estimation in a pulse labelled system

Stochastic compartmental analysis with examples of estimation in pulse labelled syste

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

The Grove and Flagg Plantations Survey

https://scholarcommons.sc.edu/archanth_books/1061/thumbnail.jp

A computer program for the mixed analysis of variance model based on maximum likelihood

Computer program for mixed analysis of variance model based on maximum likelihoo