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Statistics and Probability

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Theses and Dissertations

1993

Articles 1 - 9 of 9

Full-Text Articles in Physical Sciences and Mathematics

Developing Prediction Regions For A Time Series Model For Hurricane Forecasting, William Cheman Dec 1993

Developing Prediction Regions For A Time Series Model For Hurricane Forecasting, William Cheman

Theses and Dissertations

In this thesis, a class of time series models for forecasting a hurricanes future position based on its previous positions and a generalized model of hurricane motion are examined and extended. Results of a literature review suggest that meteorological models continue to increase in complexity while few statistical approaches, such as linear regression, have been successfully applied. An exception is provided by a certain class of time series models that appear to forecast storms almost as well as current meteorological models without their tremendous complexity. A suggestion for enhancing the performance of these time series models is pursued through an …

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Solving The Ranking And Selection Indifference-Zone Formulation For Normal Distributions Using Computer Software, Catherine A. Poston Dec 1993

Solving The Ranking And Selection Indifference-Zone Formulation For Normal Distributions Using Computer Software, Catherine A. Poston

Theses and Dissertations

Ranking and selection procedures are statistical methods used to compare and choose the best among a group of similar statistically distributed populations. The two predominant approaches to solving ranking and selection problems are Guptas subset selection formulation and Bechhofers indifference- zone formulation. For the indifference-zone formulation where the populations have equal sample sizes, Barr and Rizvi developed an integral expression of the probability of correct selection PCS. Given appropriate parameters, the integral expression can be solved to determine the common sample size required to attain a desired PCS. Tables with selected solutions to the integral expression are available for a …

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A Parametric Regression Of The Cost Of Base Realignment Action (Cobra) Model, Douglas Hardman, Michael Nelson Sep 1993

A Parametric Regression Of The Cost Of Base Realignment Action (Cobra) Model, Douglas Hardman, Michael Nelson

Theses and Dissertations

This study develops a parametric model that is capable of generating accurate estimates of the costs to close Air Force installations. The new model is based upon, but much simpler to use than, the Cost of Base Realignment Action (COBRA) model. COBRA is an economic cost analysis model that requires a minimum of 250 inputs and as many as 700 inputs. The new parametric model requires just 10 input variables and was developed using least squares multiple regression. Comparison of the new parametric model to COBRA indicates that it captures 91 percent of the variance in cost estimates generated by …

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Identification Of Significant Outliers In Time Series Data, Keri L. Robinson Mar 1993

Identification Of Significant Outliers In Time Series Data, Keri L. Robinson

Theses and Dissertations

This thesis examines the feasibility of using least median of squares (LMS) procedure applied to a reweighted least squares (RLS) autoregression model to identify significant outliers in time series data. The time series were analyzed for data points that were outliers. In order to perform detailed analysis on an outlier. the analyst must be able to determine that an outlier data point is significantly different from normally distributed data. This thesis examines a new method for identifying these outliers. Data from the field were characterized and fit with time series models using an autoregressive reweighted least squares routine (ARRLS) derived …

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A New Goodness-Of-Fit Test For The Weibull Distribution Based On Spacings, Mark C. Coppa Mar 1993

A New Goodness-Of-Fit Test For The Weibull Distribution Based On Spacings, Mark C. Coppa

Theses and Dissertations

The critical values for a new goodness-of-fit test based on spacings are generated for the Weibull distribution when the shape parameter is known. The critical values are used for testing whether a set of observations follow a Weibull distribution when the scale and location parameters are unknown. A Monte Carlo simulation with 10,000 iterations is used to generate the critical values for sample sizes 5(5)35 at shape parameters k equal to 0.5(0.5)1.5 and for sample sizes 5(5)20 at shape parameters k = 2.0(1.0)4.0. A Monte Carlo power study of the Z* test statistic using 5000 iterations is accomplished using nine …

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Modified Anderson-Darling And Cramer-Von Mises Goodness-Of-Fit Tests For The Normal Distribution, David A. Gwinn Sr. Mar 1993

Modified Anderson-Darling And Cramer-Von Mises Goodness-Of-Fit Tests For The Normal Distribution, David A. Gwinn Sr.

Theses and Dissertations

New techniques for calculating goodness-of-fit statistics for normal distributions with parameters estimated from the sample are investigated. Samples are generated for a Normal(0,1) distribution. Critical values are calculated for five modifications to the Anderson-Darling statistic and five modifications to the Cramer-Von Mises statistic. An extensive power study is done to test the power of the new statistics versus the power of the unmodified statistics. Powers of six of the new statistics show minimal to no improvement, two of the new statistics show a marked decrease in power, and two of the new statistics show an overall increase in power over …

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A Comparison Of Variable Selection Criteria For Multiple Linear Regression: A Second Simulation Study, David P. Woollard Mar 1993

A Comparison Of Variable Selection Criteria For Multiple Linear Regression: A Second Simulation Study, David P. Woollard

Theses and Dissertations

This thesis implements a variable selection method proposed by Alan J. Miller, and makes an extension of Ross J. Hansen's 1988 thesis research by comparing the methods he examined: Minimum MSE, Minimum Sp, and Minimum Cp with Miller's method. Response Surface methodology is employed with two performance measures: the percentage of correct variables in a model and the Theoretical Mean Squared Error of Prediction (TMSEP). Each technique is applied on generated data with known multicollinearities, variances, random predictors, and sample sizes. Both performance measures are computed for models selected under each technique. A full factorial design using each performance measure …

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A Modified Chi-Squared Goodness-Of-Fit Test For The Three-Parameter Gamma Distribution With Unknown Parameters, Thomas J. Sterle Mar 1993

A Modified Chi-Squared Goodness-Of-Fit Test For The Three-Parameter Gamma Distribution With Unknown Parameters, Thomas J. Sterle

Theses and Dissertations

A modified chi-squared goodness-of-fit test was created for the gamma distribution in the case where all three parameters must be estimated from the sample. Critical values are generated using a Monte Carlo simulation procedure with 5000 repetitions each. Random samples of 8 different sizes were drawn from gamma distributions with shape parameters 1, 1.5, 2., and 2.5. The shape, scale, and location parameters were then estimated from each sample, using an iterative technique combining the maximum likelihood and minimum distance methods, enabling, computation of the chi-squared statistics and critical values. The same process is used to generate random samples, parameter …

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A Modified Anderson Darling Goodness-Of-Fit Test For The Gamma Distribution With Unknown Scale And Location Parameters, Tamer Ozmen Mar 1993

A Modified Anderson Darling Goodness-Of-Fit Test For The Gamma Distribution With Unknown Scale And Location Parameters, Tamer Ozmen

Theses and Dissertations

A new modified Anderson-Darling goodness-of-fit test is introduced for the three-parameter Gamma distribution when the location parameter is found by minimum distance estimation and scale parameter by maximum likelihood estimation. Monte Carlo simulation studies were performed to calculate the critical values for A-D test when A-D statistic is minimized. These critical values are then used for testing whether a set of observations follows a Gamma distribution when the scale and location parameters axe unspecified and are estimated from the sample. Functional relationship between the critical values of A-D is also examined for each shape parameter by the variables, sample size …

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