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21 
Estimation procedures for ordered categorical dataPemberton, J. D. January 1984 (has links)
No description available.

22 
Parametric estimation of uniform effect with normal error.January 1980 (has links)
by Yuen WahKong. / Thesis (M.Phil.)Chinese University of Hong Kong, 1980. / Bibliography: leaves 3738.

23 
Constrained estimation in multiple groups covariance structure model.January 1981 (has links)
by Kwokleung Tsui. / Thesis (M.Phil.)Chinese University of Hong Kong, 1981. / Bibliography: leaves 3941.

24 
Onepass procedures of unequal probability sampling.January 1993 (has links)
by Kwokfai Lee. / Thesis (M.Phil.)Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 8586). / Chapter CHAPTER 1  INTRODUCTION  p.1 / Chapter §1.1  Unequal probabilities sampling schemes without replacement  p.1 / Chapter §1.2  Estimation Problems in unequal probabilities sampling scheme without replacement  p.3 / Chapter §1.3  Classification of unequal probabilities sampling schemes without replacement  p.5 / Chapter CHAPTER 2  ONEPASS ALGORITHMS  p.9 / Chapter §2.1  Characteristics of onepass algorithms  p.9 / Chapter §2.2  Existing onepass algorithms  p.10 / Chapter §2.2.1  Chao's onepass algorithm / Chapter §2.2.2  Other algorithms / Chapter §2.3  Second order inclusion probabilities  p.14 / Chapter CHAPTER 3  A NEW ONEPASS ALGORITHM  p.17 / Chapter §3.1  Introduction  p.17 / Chapter §3.2  Examination of all possible cases  p.20 / Chapter §3.3  Initialization  p.57 / Chapter §3.4  Final step  p.61 / Chapter §3.5  Theorems  p.64 / Chapter §3.6  Worked example  p.76 / Chapter CHAPTER 4  CONCLUSION  p.83 / References  p.85

25 
Performance evaluation of techniques for time delay estimationScarbrough, Kent N January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries

26 
A comparison of two estimators of the variance in the twofactor multiplicative interaction modelWasserstein, Ronald Lee January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries

27 
The effect of random dosages on probit analysisMorrill, Bruce January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries

28 
Finite sample performance of nonparametric regression estimators : the case of additive and parametric covariance modelsYang, Ke 11 July 2005 (has links)
This dissertation is composed of three essays regarding the finite sample properties of estimators
for nonparametric models.
In the first essay we investigate the finite sample performances of four estimators for additive
nonparametric regression models  the backfitting Bestimator, the marginal integration Mestimator
and two versions of a two stage 2Sestimator, the first proposed by Kim, Linton and
Hengartner (1999) and the second which we propose in this essay. We derive the conditional
bias and variance of the 2S estimators and suggest a procedure to obtain optimal bandwidths
that minimize an asymptotic approximation of the mean average squared errors (AMASE). We
are particularly concerned with the performance of these estimators when bandwidth selection is
done based on data driven methods. We compare the estimators' performances based on various
bandwidth selection procedures that are currently available in the literature as well as with the
procedures proposed herein via a Monte Carlo study.
The second essay is concerned with some recently proposed kernel estimators for panel data
models. These estimators include the local linear estimator, the quasilikelihood estimator, the prewhitening estimators, and the marginal kernel estimator. We focus on the finite sample properties
of the above mentioned estimators on random effects panel data models with different withinsubject
correlation structures. For each estimator, we use the asymptotic mean average squared
errors (AMASE) as the criterion function to select the bandwidth. The relative performance of the
test estimators are compared based on their average squared errors, average biases and variances.
The third essay is concerned with the finite sample properties of estimators for nonparametric
regression models with autoregressive errors. The estimators studied are: the local linear,
the quasilikelihood, and two prewhitening estimators. Bandwidths are selected based on the
minimization of the asymptotic mean average squared errors (AMASE) for each estimator. Two
regression functions and multiple variants of autoregressive processes are employed in the simulation.
Comparison of the relative performances is based mainly on the estimators' average squared
errors (ASE). Our ultimate objective is to provide an extensive finite sample comparison among
competing estimators with a practically selected bandwidth. / Graduation date: 2006

29 
Post stratified estimation using a known auxiliary variableBedier, Mostafa Abdellatif 18 September 1989 (has links)
Post stratification is considered desirable in sample surveys for two
reasons  it reduces the mean squared error when averaged over all possible
samples, and it reduces the conditional bias when conditioned on stratum
sample sizes. The problem studied in this thesis is post stratified estimation
of a finite population mean when there is a known auxiliary variable for each
population unit.
The primary direction of the thesis follows the lines of Holt and
Smith (1979). A method is given for using the auxiliary variable in selection
of the stratum boundaries and, using this approach to determine strata, to
compare post stratified estimates with the self weighting estimates from
the analytical and empirical points of view. Estimates studied are: the post
stratified mean, the post stratified combined ratio, and the post stratified
separate ratio. The thesis contains simulation results that explore the
distributions of the self weighting estimates, and the post stratified estimates
using conditional and unconditional inferences. The correct coverage
properties of the confidence intervals are compared and the design effect,
i.e. the ratio of the variance of the self weighting to the variance of post
stratified estimates, is calculated from the samples and its distribution
explored by the simulation study for several real and artificial
populations. The confidence intervals of post stratified estimates
using conditional variances had good coverage properties for each
sample configuration used, and hence the correct coverage property over
all possible samples provided that the Central Limit Theorem was applied.
The comparisons indicated that post stratification is an effective
approach when the boundaries are obtained based on proper stratification using
an auxiliary variable. Moreover it is more efficient than estimation based
on simple random sampling in reducing the mean squared error.
Finally, there is strong evidence that the post stratified estimates are
robust against poorly distributed samples, whereas empirical investigations
suggested that the self weighting estimates are very poor when the samples are
unbalanced. / Graduation date: 1990

30 
Least squares and adaptive multirate filtering /Hawes, Anthony H. January 2003 (has links) (PDF)
Thesis (M.S. in Electrical Engineering)Naval Postgraduate School, September 2003. / Thesis advisor(s): Charles W. Therrien, Roberto Cristi. Includes bibliographical references (p. 45). Also available online.

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