1 
Methods of constructing confidence regions for parameters in the power transformation models.January 1994 (has links)
by Waileung Li. / Thesis (M.Phil.)Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 7477). / Chapter Chapter 1  Introduction  p.1 / Chapter § 1.1  Why transformation of variables in regression analysis is needed?  p.1 / Chapter § 1.2  Suggested functional transformation  BoxCox Transformation  p.3 / Chapter § 1.3  Methodology  p.5 / Chapter § 1.4  General theory of constructing asymptotic confidence intervals and confidence regions  p.9 / Chapter § 1.4.1  Method based on the loglikelihood ratio statistic  p.9 / Chapter § 1.4.2  Method based on the asymptotic normality of the maximum likelihood estimates  p.13 / Chapter § 1.4.3  Method based on the score statistic  p.15 / Chapter § 1.5  General theory of constructing exact confidence intervals and confidence regions  p.17 / Chapter § 1.6  Summary  p.23 / Chapter Chapter 2  Confidence Intervals for the nonlinear parameter λ in the BoxCox transformation models  p.24 / Chapter § 2.1  Confidence intervals based on the loglikelihood ratio statistics  p.26 / Chapter § 2.1.1  Asymptotically equivalent forms for constructing confidence intervals based on the loglikelihood ratio statistics  p.30 / Chapter § 2.2  Confidence intervals based on the asymptotic normality of the maximum likelihood estimates  p.31 / Chapter § 2.3  Confidence intervals based on the score statistics  p.35 / Chapter § 2.4  Confidence intervals based on the exact test  p.36 / Chapter § 2.5  Small simulation studies of constructing confidence intervals for A based on the four different methods  p.37 / Chapter § 2.5.1  Design of the simulation studies  p.40 / Chapter § 2.5.2  Simulation results  p.41 / Chapter § 2.6  Summary  p.44 / Chapter Chapter 3  Confidence Regions for the parameters in the BoxCox transformation models  p.45 / Chapter § 3.1  Confidence regions based on the loglikelihood ratio statistics  p.45 / Chapter § 3.1.1  "Confidence region for (λ,ζ1）based on the loglikelihood ratio statistics"  p.46 / Chapter § 3.1.2  Confidence region for (ζ1）based on the loglikelihood ratio statistics  p.51 / Chapter § 3.2  Confidence regions based on the asymptotic normality of the maximum likelihood estimates  p.53 / Chapter § 3.2.1  "Confidence region for (λ,ζ1）based on the asymptotic normality of the maximum likelihood estimates"  p.53 / Chapter § 3.2.2  Confidence region for (ζ1）based on the asymptotic normality of the maximum likelihood estimates  p.57 / Chapter § 3.3  Confidence regions based on the score statistics  p.58 / Chapter § 3.3.1  "Confidence region for (λ,ζ1） based on the score statistic"  p.59 / Chapter § 3.3.2  Confidence region for (ζ1 ) based on the score statistic  p.60 / Chapter § 3.4  Confidence region based on the exact test  p.61 / Chapter § 3.5  Small simulation studies of constructing confidence regions for the parameters of interest based on the four different methods  p.62 / Chapter Chapter 4  Robustness and Discussion  p.67 / Chapter §4.1  Contamination normal distribution  p.67 / Chapter § 4.1.1  Confidence intervals for the non linear parameter λ based on the contamination normal distribution of error terms  p.68 / Chapter § 4.1.2  Confidence regions for the parameters of interest based on the contamination normal distribution of the error terms  p.70 / Chapter § 4.2  Summary  p.72 / References  p.74 / Figures / Appendix A / Appendix B / Appendix C / Appendix D

2 
Confidence intervals for the risk ratio under inverse sampling.January 2005 (has links)
Ip Wing Yiu. / Thesis (M.Phil.)Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaf 44). / Abstracts in English and Chinese. / Chapter 1  Introduction  p.1 / Chapter 1.1  Introduction  p.1 / Chapter 1.2  Background  p.1 / Chapter 1.3  Objective  p.3 / Chapter 1.4  Scope of the thesis  p.3 / Chapter 2  Basic Concepts  p.5 / Chapter 2.1  Inverse Sampling  p.5 / Chapter 2.2  Equivalence/ Noninferiority Testing  p.6 / Chapter 3  Inference for Risk Ratio  p.8 / Chapter 3.1  Introduction  p.8 / Chapter 3.2  Test Statistics for Risk Ratio  p.8 / Chapter 3.3  Consistent Estimators of π  p.12 / Chapter 4  Confidence Interval  p.16 / Chapter 4.1  Introduction  p.16 / Chapter 4.2  TostBased Confidence Interval  p.17 / Chapter 4.3  Using samplebased estimates  p.18 / Chapter 5  Simulation  p.21 / Chapter 5.1  Introduction  p.21 / Chapter 5.2  Simulation Procedures  p.21 / Chapter 5.3  Simulation Results  p.23 / Chapter 6  Conclusion  p.27 / Appendix  p.29 / Chapter A.  Equation derviation  p.29 / Chapter A1.  Equation derviation 1  p.29 / Chapter A2.  Equation derviation 2  p.31 / Chapter B.  Table  p.32 / References  p.44

3 
Confidence intervals for variance componentsPurdy, Kathleen G. 08 May 1998 (has links)
Measuring the source and magnitude of components of variation has important
applications in industrial, environmental and biological studies. This thesis considers
the problem of constructing confidence intervals for variance components in Gaussian
mixed linear models. A number of methods based on the usual ANOVA mean squares
have been proposed for constructing confidence intervals for variance components in
balanced mixed models. Some authors have suggested extending balanced model
procedures to unbalanced models by replacing the ANOVA mean squares with mean
squares from an unweighted means ANOVA. However, the unweighted means
ANOVA is only defined for a few specific mixed models. In Chapter 2 we define a
generalization of the unweighted means ANOVA for the three variance component
mixed linear model and illustrate how the mean squares from this ANOVA may be used
to construct confidence intervals for variance components. Computer simulations
indicate that the proposed procedure gives intervals that are generally consistent with the
stated confidence level, except in the case of extremely unbalanced designs. A set of
statistics that can be used as an alternative to the generalized unweighted mean squares
is developed in Chapter 3. The intervals constructed with these statistics have better
coverage probability and are often narrower than the intervals constructed with the
generalized unweighted mean squares. / Graduation date: 1998

4 
Uniformly consistent bootstrap confidence intervalsYu, Zhuqing., 俞翥清. January 2012 (has links)
The bootstrap methods are widely used for constructing confidence intervals.
However, the conventional bootstrap fails to be consistent under some nonstandard
circumstances. The m out of n bootstrap is usually adopted to restore
consistency, provided that a correct convergence rate can be specified for the
plugin estimators. In this thesis, we reinvestigate the asymptotic properties of
the bootstrap in a movingparameter framework in which the underlying distribution
is allowed to depend on n. We consider the problem of setting uniformly
consistent confidence intervals for two nonregular cases: (1) the smooth function
models with vanishing derivatives; and (2) the Mestimation with nonregular
conditions.
Under the movingparameter setup, neither the conventional bootstrap nor
the m out of n bootstrap is shown uniformly consistent over the whole parameter space. The results reflect to some extent finitesample anomalies that cannot be
explained by conventional, fixedparameter, asymptotics. We propose a weighted
bootstrap procedure for constructing uniformly consistent bootstrap confidence
intervals, which does not require explicit specification of the convergence rate
of the plugin estimator. Under the smooth function models, we also propose
a modified n out of n bootstrap procedure in special cases where the smooth
function is applied to estimators that are uniformly bootstrappable. The estimating
function bootstrap is also successfully employed for the latter model
and enjoys computational advantages over the weighted bootstrap. We illustrate
our findings by comparing the finitesample coverage performances of the different
bootstrap procedures. The stable performance of the proposed methods,
contrasts sharply with the erratic coverages of the n out of n and m out of n
bootstrap intervals, a result in agreement with our theoretical findings. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

5 
The construction of joint confidence sets for the comparison of two exponential distributionsRobinson, Jennifer 12 1900 (has links)
No description available.

6 
The efficiency of nonparametric inference methods based on confidence interval lengthsdeCamp, Philip Draper 05 1900 (has links)
No description available.

7 
Comparing the overlapping of two independent confidence intervals with a single confidence interval for two normal population parametersHuang, Chingying, Maghsoodloo, Saeed, January 2008 (has links)
Thesis (Ph. D.)Auburn University. / Abstract. Vita. Includes bibliographical references (p. 132135).

8 
Confidence intervals for computable general equilibrium modelsTuladhar, Sugandha Dhar, January 2003 (has links) (PDF)
Thesis (Ph. D.)University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

9 
Estimation from data representing a sample of curvesAuguste, Anna L. Bunea, Florentina. January 2006 (has links)
Thesis (Ph. D.)Florida State University, 2006. / Advisor: Florentina Bunea, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept.18, 2006). Document formatted into pages; contains xi, 89 pages. Includes bibliographical references.

10 
Efficient confidence interval methodologies for the noncentrality parameters of noncentral TdistributionsKim, Jong Phil. January 2007 (has links)
Thesis (Ph. D.)Industrial and Systems Engineering, Georgia Institute of Technology, 2007. / Lewis VanBrackle, Committee Member ; Brani Vidakovic, Committee Member ; Anthony J. Hayter, Committee Chair ; Nicholeta Serban, Committee Member ; Alexander Shapiro, Committee Member.

Page generated in 0.142 seconds