Global ETD Search

1	The impact of misspecification of nuisance parameters on test for homogeneity in zero-inflated Poisson model: a simulation study Gao, Siyu January 1900 (has links) Master of Science / Department of Statistics / Wei-Wen Hsu / The zero-inflated Poisson (ZIP) model consists of a Poisson model and a degenerate distribution at zero. Under this model, zero counts are generated from two sources, representing a heterogeneity in the population. In practice, it is often interested to evaluate this heterogeneity is consistent with the observed data or not. Most of the existing methodologies to examine this heterogeneity are often assuming that the Poisson mean is a function of nuisance parameters which are simply the coefficients associated with covariates. However, these nuisance parameters can be misspecified when performing these methodologies. As a result, the validity and the power of the test may be affected. Such impact of misspecification has not been discussed in the literature. This report primarily focuses on investigating the impact of misspecification on the performance of score test for homogeneity in ZIP models. Through an intensive simulation study, we find that: 1) under misspecification, the limiting distribution of the score test statistic under the null no longer follows a chi-squared distribution. A parametric bootstrap methodology is suggested to use to find the true null limiting distribution of the score test statistic; 2) the power of the test decreases as the number of covariates in the Poisson mean increases. The test with a constant Poisson mean has the highest power, even compared to the test with a well-specified mean. At last, simulation results are applied to the Wuhan Inpatient Care Insurance data which contain excess zeros. zero-inflated Poisson model score test misspecification nuisance parameter Statistics (0463)
2	Testy dobré shody při rušivých parametrech / Goodness of fit tests with nuisance parameters Baňasová, Barbora January 2015 (has links) This thesis deals with the goodness of fit tests in nonparametric model in the presence of unknown parameters of the probability distribution. The first part is devoted to understanding of the theoretical basis. We compare two methodologies for the construction of test statistics with application of empirical characteristic and empirical distribution functions. We use kernel estimates of regression functions and parametric bootstrap method to approximate the critical values of the tests. In the second part of the thesis, the work is complemented with the simulation study for different choices of weighting functions and parameters. Finally we illustrate the use and the comparison of goodness of fit tests on the example with the real data set. Powered by TCPDF (www.tcpdf.org)
3	Inference for the intrinsic separation among distributions which may differ in location and scale Ling, Yan January 1900 (has links) Doctor of Philosophy / Department of Statistics / Paul I. Nelson / The null hypothesis of equal distributions, H0 : F1[equals]F2[equals]...[equals]FK , is commonly used to compare two or more treatments based on data consisting of independent random samples. Using this approach, evidence of a difference among the treatments may be reported even though from a practical standpoint their effects are indistinguishable, a longstanding problem in hypothesis testing. The concept of effect size is widely used in the social sciences to deal with this issue by computing a unit-free estimate of the magnitude of the departure from H0 in terms of a change in location. I extend this approach by replacing H0 with hypotheses H0* that state that the distributions {Fi} are possibly different in location and or scale, but close, so that rejection provides evidence that at least one treatment has an important practical effect. Assessing statistical significance under H0* is difficult and typically requires inference in the presence of nuisance parameters. I will use frequentist, Bayesian and Fiducial modes of inference to obtain approximate tests and carry out simulation studies of their behavior in terms of size and power. In some cases a bootstrap will be employed. I will focus on tests based on independent random samples arising from K[greater than and equals]3 normal distributions not required to have the same variances to generalize the K[equals]2 sample parameter P(X1>X2) and non-centrality type parameters that arise in testing for the equality of means. intrinsic separation parameter (ISP) normal distribution nuisance parameter p-value average p-value parametric bootstrap test Statistics (0463)
4	Improved estimation in threshold regression with applications to price transmission modeling / Verbessertes Schätzen von Threshold Regressionsmodellen mit Anwendungen in der Preistransmissionsanalyse Greb, Friederike 30 January 2012 (has links) No description available. 630 Landwirtschaft Veterinärmedizin YI 000 LM 011 EGC XXX Agricultural Sciences Bayes Schätzer Empirical Bayes Marktintegration Preistransmission Störparameter Threshold Fehlerkorrekturmodell Threshold Regression Threshold Schätzer Bayesian estimator empirical Bayes market integration nuisance parameter price transmission spatial arbitrage threshold estimation threshold regression TVECM 83.66 83.03 31.73

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