Other Statistics and Probability Commons^™

Smart Sampling Of Noble Gases To Detect Underground Nuclear Explosions, Lindsey M. Skelton, Steven Hunter, Charles Carrigan

STAR Program Research Presentations

One element of the Comprehensive Nuclear Test Ban Treaty (CTBT) is the provision for an on site inspection (OSI). The purpose of an OSI is to monitor for the occurrence of an underground nuclear explosion (UNE) in violation of the treaty. Detection of certain rare radioactive noble gases transported to the surface can be an excellent indicator of a UNE. These gases can be very difficult to capture and require specialized sampling methods. This study aims to determine an algorithm that will increase the efficiency of the subsurface gas sampling technique being used to detect UNEs. Continuous sampling of subsurface …

Application Of Inter-Die Rank Statistics In Defect Detection, Vivek Bakshi

Dissertations and Theses

This thesis presents a statistical method to identify the test escapes. Test often acquires parametric measurements as a function of logical state of a chip. The usual method of classifying chips as pass or fail is to compare each state measurement to a test limit. Subtle manufacturing defects are escaping the test limits due to process variations in deep sub-micron technologies which results in mixing of healthy and faulty parametric test measurements. This thesis identifies the chips with subtle defects by using rank order of the parametric measurements. A hypothesis is developed that a defect is likely to disturb the …

Why Divide By (N-1) For Sample Standard Deviation?, Paul Savory

Department of 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.

Fluid Flow In Micro-Channels: A Stochastic Approach, Hilda Marino Black

Doctoral Dissertations

In this study free molecular flow in a micro-channel was modeled using a stochastic approach, namely the Kolmogorov forward equation in three dimensions. Model equations were discretized using Central Difference and Backward Difference methods and solved using the Jacobi method. Parameters were used that reflect the characteristic geometry of experimental work performed at the Louisiana Tech University Institute for Micromanufacturing.

The solution to the model equations provided the probability density function of the distance traveled by a particle in the micro-channel. From this distribution we obtained the distribution of the residence time of a particle in the micro-channel. Knowledge of …

Cramer-Rao Bound And Optimal Amplitude Estimator Of Superimposed Sinusoidal Signals With Unknown Frequencies, Shaohui Jia

Doctoral Dissertations

This dissertation addresses optimally estimating the amplitudes of superimposed sinusoidal signals with unknown frequencies. The Cramer-Rao Bound of estimating the amplitudes in white Gaussian noise is given, and the maximum likelihood estimator of the amplitudes in this case is shown to be asymptotically efficient at high signal to noise ratio but finite sample size. Applying the theoretical results to signal resolutions, it is shown that the optimal resolution of multiple signals using a finite sample is given by the maximum likelihood estimator of the amplitudes of signals.

Reply To Valverde, Paul B. Thompson

RISK: Health, Safety & Environment (1990-2002)

Professor Thompson responds to Valverde's argument, in the last issue, that his approach to Risk puts too much emphasis on the distinction between Risk subjectivism and Risk objectivism. In doing so, he asserts, inter alia, that anchoring Risk judgments in a probabilistic framework does not go far enough in rejecting reigning Risk-analysis notions of "real Risk."