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Carrillo, Rafael E. – ProQuest LLC, 2012
Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…
Descriptors: Mathematics, Computation, Sampling, Data Collection
Larsson, B. – 1972
An experimental study of the efficiency of human information processing is based on the Bayesian model for simple hypothesis testing with fixed binomial sampling. Each of 60 subjects is analyzed with separate ANOVAs focusing on two efficiency variables. Sample size and critical value are also analyzed. Subjects show very different utilization of…
Descriptors: Bayesian Statistics, Cognitive Processes, Hypothesis Testing, Information Processing
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Hill, Richard K. – 1974
When norming tests, it may be preferable to use the matrix sampling technique. The results from the samples may be used to estimate what the distribution of scores would have been if each subject had taken all the items. This paper compares four methods for making these estimates. The sample size made it possible to compare the techniques in a…
Descriptors: Bayesian Statistics, Comparative Analysis, Data Analysis, Item Sampling
Coffman, William E.; Shigemasu, Kazuo – 1978
Appraisal of a school's relative effectiveness is complicated by: (1) the need to control for input differences; (2) measurement error in input measures; and (3) small sample size within schools. This study compares the performance of two successive cohorts in 19 schools in a small midwestern city on the five Iowa Tests of Basic Skills using both…
Descriptors: Academic Achievement, Accountability, Achievement Gains, Analysis of Covariance