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Articles 1 - 5 of 5
Full-Text Articles in Other Statistics and Probability
Application Of Optimization Techniques To Spectrally Modulated, Spectrally Encoded Waveform Design, Todd W. Beard
Application Of Optimization Techniques To Spectrally Modulated, Spectrally Encoded Waveform Design, Todd W. Beard
Theses and Dissertations
A design process is demonstrated for a coexistent scenario containing Spectrally Modulated, Spectrally Encoded (SMSE) and Direct Sequence Spread Spectrum (DSSS) signals. Coexistent SMSE-DSSS designs are addressed under both perfect and imperfect DSSS code tracking conditions using a non-coherent delay-lock loop (DLL). Under both conditions, the number of SMSE subcarriers and subcarrier spacing are the optimization variables of interest. For perfect DLL code tracking conditions, the GA and RSM optimization processes are considered independently with the objective function being end-to-end DSSS bit error rate. A hybrid GA-RSM optimization process is used under more realistic imperfect DLL code tracking conditions. In …
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory
Industrial and Management Systems Engineering: Instructional Materials
In statistics, the sample standard deviation is a widely used measure of the variability or dispersion of a data set. The standard deviation of a data set is the square root of its variance. In calculating the sample standard deviation, the divisor is the number of samples in the data set minus one (n-1) rather than n. This often confuses students. This paper offers a quick overview of why the divisor is (n-1) for calculating the sample standard deviation.
J. K. Ghosh’S Contribution To Statistics: A Brief Outline, Bertrand Clarke, Subhashis Ghosal
J. K. Ghosh’S Contribution To Statistics: A Brief Outline, Bertrand Clarke, Subhashis Ghosal
Department of Statistics: Faculty Publications
Professor Jayanta Kumar Ghosh has contributed massively to various areas of Statistics over the last five decades. Here, we survey some of his most important contributions. In roughly chronological order, we discuss his major results in the areas of sequential analysis, foundations, asymptotics, and Bayesian inference. It is seen that he progressed from thinking about data points, to thinking about data summarization, to the limiting cases of data summarization in as they relate to parameter estimation, and then to more general aspects of modeling including prior and model selection.
An Ensemble Approach To Improved Prediction From Multitype Data, Jennifer Clarke
An Ensemble Approach To Improved Prediction From Multitype Data, Jennifer Clarke
Department of Statistics: Faculty Publications
We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based on logic trees. The results of these models are then compared to the results of a model based on existing data with the objective of combining model results to achieve the most accurate predictions. The combination of model predictions is aided by the use of support vector machines to identify subspaces of …
A Bayesian Test For Excess Zeros In A Zero-Inflated Power Series Distribution, Archan Bhattacharya, Bertrand S. Clarke, Gauri S. Datta
A Bayesian Test For Excess Zeros In A Zero-Inflated Power Series Distribution, Archan Bhattacharya, Bertrand S. Clarke, Gauri S. Datta
Department of Statistics: Faculty Publications
Power series distributions form a useful subclass of one-parameter discrete exponential families suitable for modeling count data. A zero-inflated power series distribution is a mixture of a power series distribution and a degenerate distribution at zero, with a mixing probability p for the degenerate distribution. This distribution is useful for modeling count data that may have extra zeros. One question is whether the mixture model can be reduced to the power series portion, corresponding to p = 0, or whether there are so many zeros in the data that zero inflation relative to the pure power series distribution must be …