Mathematical statistics

With the publication of Shor's quantum algorithm for solving discrete logarithms in
finite cyclic groups, a need for new cryptographic primitives arose; namely, for more secure
primitives that would prevail in the post-quantum era.
The aim of this dissertation is to exploit some hard problems arising from group theory
for use in cryptography. Over the years, there have been many such proposals. We first look
at two recently proposed schemes based on some form of a generalization of the discrete
logari thm problem (DLP), identify their weaknesses, and cryptanalyze them.
By applying the exper tise gained from the above cryptanalyses, we define our own
generalization of the DLP to arbitrary finite groups. We show that such a definition leads
to the design of signature schemes and pseudo-random number generators with provable
security under a security assumption based on a group theoretic problem. In particular,
our security assumption is based on the hardness of factorizing elements of the projective
special linear group over a finite field in some representations. We construct a one-way
function based on this group theoretic assumption and provide a security proof.

Determining the variance of a statistic (such as the sample median) can be
difficult. Various methods of Bootstrapping (re-sampling with replacement) were
used to estimate variance of one or more statistics based on a single sample. This
estimator was compared to the empirical estimators based on repeated simulations of
various sample sizes from a given distribution. Of particular interest was which of the
methods of Bootstrapping were most effective with a dependent data set. Different
degrees of dependency were used for the simulations with dependent data.

Trends in streamflow extremes at a regional scale linked to the possible influences of four major oceanic-atmospheric oscillations are analyzed in this study. Oscillations considered include: El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The main emphasis is low flows in the South-Atlantic Gulf region of the United States. Several standard drought indices of low flow extremes during two different phases (warm/positive and cool/negative) of these oscillations are evaluated. Long-term streamflow data at 43 USGS sites in the region from the Hydro-Climatic Data Network that are least affected by anthropogenic influences are used for analysis. Results show that for ENSO, low flow indices were more likely to occur during La Niña phase; however, longer deficits were more likely during El Niño phase. Results also show that for PDO (AMO), all (most) low flow indices occur during the cool (warm) phase.

This thesis contains two parts. The first part derives the Bayesian estimator of
the parameters in a piecewise exponential Cox proportional hazard regression model,
with one unknown change point for a right censored survival data. The second part
surveys the applications of change point problems to various types of data, such as
long-term survival data, longitudinal data and time series data. Furthermore, the
proposed method is then used to analyse a real survival data.

Let L be a uniserial ring of length n, with maximal ideal r , and finite residue field Λ/ r . We consider Λ-modules which possess a finite composition series. We note that a Λ-module has the form B ≅ ⨁i=1m Λ/ rli , where the type of B is the partition l = ( l1,&ldots;,lm ) denoted by t(B). For Λ-modules A, B, C with t(A) = m , t(B) = l , t(C) = n , if A ⊆ B, and B/A ≅ C, we define GBAC = |{U ⊆ B : U ≅ A and B/U ≅ C}|. We show that GBAC = MonoA,B,C Aut A = | S (A, B, C)/∼| = glmn (q), where |Λ/ r | = q, and the last equality comes from evaluating the Hall polynomial glmn (t) ∈ Z [t] at q, as stated in Hall's Theorem. We note that GBAC make up the coefficients of the Hall algebra. We provide a proof that the Hall algebra is a commutative and associative ring. Using the property of associativity of the Hall algebra and I. G. MacDonald's formula: glb1l =qnl -nb-n 1li≥ 1l'i -b'i,b' i-l'i+1 q-1 we develop a procedure to generate arbitrary Hall polynomials and we compute g6,4,2 4,24,2 (q).

In this thesis a comparative study of treatment and carryover effects is performed for five statistical tests as they apply to a three treatment, three period crossover design. These tests are two likelihood based tests, namely, Ordinary Least Squares and Modified F-Test Approximation; two non parametric tests, the one of Bellavance and Tardif (1995) and the other of Ohrvik (1998); and The Generalized Estimating Equations test. This crossover design consists of six possible treatment sequences, (123, 132, 213, 231, 312 and 321) made up of three treatments, say 1, 2 and 3, being assigned to six subjects per group, for n groups. For each sequence, observations are taken for each period following the treatment. The statistical tests were used to determine treatment effects and carryover effects. Data is simulated for group sizes of 4, 8, 12 and 24 subjects per sequence, and different covariance structures. For each simulation, 2000 independent samples were generated and significance tests using the above five methods were carried out. The empirical percentage of Type I error for each test was defined as the proportion of p-values smaller or equal to a specified nominal alpha level. Alpha levels of 0.01%, 0.05% and 0.1% were chosen in this study.

The focus of this thesis is to statistically model violent crime rates against population over the years 1960-2009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression coefficients based upon the cubic spline model and the knots chosen by the minimum/maximum algorithm. We then utilize the best subsets regression method to aid in model selection in which we find the minimum value of the Bayesian Information Criteria. Finally, we preent the R2adj as a measure of overall goodness of fit of our selected model. We have found among the fifty states and Washington D.C., 42 out of 51 showed an R2adj value that was greater than 90%. We also present an overall model of the United States. Also, we show additional applications our algorithm for data which show a non linear association. It is hoped that our method can serve as a unified model for violent crime rate over future years.

A well-known long standing problem in combinatorics and statistical mechanics is to find the generating function for self-avoiding walks (SAW) on a two-dimensional lattice, enumerated by perimeter. A SAW is a sequence of moves on a square lattice which does not visit the same point more than once. It has been considered by more than one hundred researchers in the pass one hundred years, including George Polya, Tony Guttmann, Laszlo Lovasz, Donald Knuth, Richard Stanley, Doron Zeilberger, Mireille Bousquet-Mlou, Thomas Prellberg, Neal Madras, Gordon Slade, Agnes Dit- tel, E.J. Janse van Rensburg, Harry Kesten, Stuart G. Whittington, Lincoln Chayes, Iwan Jensen, Arthur T. Benjamin, and many others. More than three hundred papers and a few volumes of books were published in this area. A SAW is interesting for simulations because its properties cannot be calculated analytically. Calculating the number of self-avoiding walks is a common computational problem. A recently proposed model called prudent self-avoiding walks (PSAW) was first introduced to the mathematics community in an unpublished manuscript of Pra, who called them exterior walks. A prudent walk is a connected path on square lattice such that, at each step, the extension of that step along its current trajectory will never intersect any previously occupied vertex. A lattice path composed of connected horizontal and vertical line segments, each passing between adjacent lattice points. We will discuss some enumerative problems in self-avoiding walks, lattice paths and walks with several step vectors. Many open problems are posted.

For years attribution research has been dominated by the ANOVA model of behavior which proposes that people construct their dispositional attributions of others by carefully comparing and weighing all situational information using mental computations similar to the processes used by researchers to analyze data. A preliminary experiment successfully determined that participants were able to distinguish differences in variability assessed across persons (high vs. low consensus) and across situations (high vs. low distinctiveness). Also, it was clear that the subjects could evaluate varying levels of situational constraint. A primary experiment administered to participants immediately following the preliminary study determined that participants grossly under-utilized those same variables when making dispositional attributions. Results gave evidence against the use of traditional ANOVA models and support for the use of the Behavior Averaging Principle of Attribution.