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Variances in meta-analysis
Title
Variances in meta-analysis.
Creator
Humphreys, Katherine L. B., Florida Atlantic University, Qian, Lianfen
Abstract/Description
Meta-analysis is a statistical method of combining many individual analyses. This thesis reviews the need for meta-analysis; the many statistical consideration facing the meta-analyst; and some of Hedges' results concerning the combined estimate of effect size with unequal weights from his 1981 and 1982 papers. Unequal weights used to combine estimates of effect size in meta-analysis are derived using the variances given by the large sample, normal approximation of the distribution of Hedges'...
Show moreMeta-analysis is a statistical method of combining many individual analyses. This thesis reviews the need for meta-analysis; the many statistical consideration facing the meta-analyst; and some of Hedges' results concerning the combined estimate of effect size with unequal weights from his 1981 and 1982 papers. Unequal weights used to combine estimates of effect size in meta-analysis are derived using the variances given by the large sample, normal approximation of the distribution of Hedges' unbiased estimates of effect sizes. These variances depend on the effect size and the sample sizes of both experimental and control groups. This creates circular definitions and calls for further estimates. This thesis analyzes the limiting normal approximation to derive a variance which is not dependent on effect size, and it provides guidelines for its use.
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Date Issued
1998
PURL
http://purl.flvc.org/fcla/dt/15544
Subject Headings
Meta-analysis, Social sciences--Statistical methods
Format
Document (PDF)
Detecting essential genes in microarray dataset with unequal number of gene probes
Title
Detecting essential genes in microarray dataset with unequal number of gene probes.
Creator
Zhang, Wei, Qian, Lianfen, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Microarray technology is a powerful approach for genomic research, which allows the monitoring of expressing profiles for tens of thousands genes in parallel and is already producing huge amounts of data. This thesis is motivated by a special microarray dataset for the bacteria Yersinia Pestis. It contains more than four thousands genes and each gene has different number of observations. The main purpose of this thesis is to detect essentially functional genes. Gene level adjusted multiple t...
Show moreMicroarray technology is a powerful approach for genomic research, which allows the monitoring of expressing profiles for tens of thousands genes in parallel and is already producing huge amounts of data. This thesis is motivated by a special microarray dataset for the bacteria Yersinia Pestis. It contains more than four thousands genes and each gene has different number of observations. The main purpose of this thesis is to detect essentially functional genes. Gene level adjusted multiple t‐test is proposed to handle the problem of unequal number of observations. Furthermore, a comparation study of our method with two other existing methods (Behrens‐Fisher method and Hotelling t‐square method) are presented. The comparison results show that our proposed methods is the best for identifying essential genes.
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Date Issued
2012
PURL
http://purl.flvc.org/fau/fd/FA00004261
Format
Document (PDF)
Wind speed analysis for Lake Okeechobee
Title
Wind speed analysis for Lake Okeechobee.
Creator
Hu, Mingyan, Florida Atlantic University, Qian, Lianfen, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
In this thesis, we analyze wind speeds collected by South Florida Water Management District at stations L001, L005, L006 and LZ40 in Lake Okeechobee from January 1995 to December 2000. There are many missing values and out-liers in this data. To impute the missing values, three different methods are used: Nearby window average imputation, Jones imputation using Kalman filter, and EM algorithm imputation. To detect outliers and remove impacts, we use ARIMA models of time series. Innovational...
Show moreIn this thesis, we analyze wind speeds collected by South Florida Water Management District at stations L001, L005, L006 and LZ40 in Lake Okeechobee from January 1995 to December 2000. There are many missing values and out-liers in this data. To impute the missing values, three different methods are used: Nearby window average imputation, Jones imputation using Kalman filter, and EM algorithm imputation. To detect outliers and remove impacts, we use ARIMA models of time series. Innovational and additive outliers are considered. It turns out that EM algorithm imputation is the best method for our wind speed data set. After imputing missing values, detecting outliers and removing the impacts, we obtain the best models for all four stations. They are all in the form of seasonal ARIMA(2, 0, 0) x (1, 0, 0)24 for the hourly wind speed data.
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Date Issued
2002
PURL
http://purl.flvc.org/fcla/dt/12883
Subject Headings
Winds--Speed--Florida--Okeechobee, Lake, Okeechobee, Lake (Fla )--Environmental conditions
Format
Document (PDF)
Bayesian approach to an exponential hazard regression model with a change point
Title
Bayesian approach to an exponential hazard regression model with a change point.
Creator
Abraha, Yonas Kidane, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
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.
Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004013
Subject Headings
Bayesian statistical decision theory, Mathematical statistics, Multivariate analysis -- Data processing
Format
Document (PDF)
DETERMINANTS OF WOMEN'S ATTITUDE TOWARDS INTIMATE PARTNER VIOLENCE: EVIDENCE FROM BANGLADESH
Title
DETERMINANTS OF WOMEN'S ATTITUDE TOWARDS INTIMATE PARTNER VIOLENCE: EVIDENCE FROM BANGLADESH.
Creator
Khan, Md Tareq Ferdous, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
This thesis uses Bangladesh Demographic and Health Survey 2014 data to identify the important determinants due to which women justification towards intimate partner violence (IPV) varies. Statistical analyses reveal that among the individual-level independent variables age at first marriage, respondent's education, decision score, religion, NGO membership, access to information, husband's education, normalized wealth score, and division indicator have significant effects on the women's...
Show moreThis thesis uses Bangladesh Demographic and Health Survey 2014 data to identify the important determinants due to which women justification towards intimate partner violence (IPV) varies. Statistical analyses reveal that among the individual-level independent variables age at first marriage, respondent's education, decision score, religion, NGO membership, access to information, husband's education, normalized wealth score, and division indicator have significant effects on the women's attitude towards IPV. It shows that other than religion, NGO membership, and division indicator, the higher the value of the variable, the lower the likelihood of justifying IPV. However, being a Muslim, NGO member, and resident of other divisions, women are found more tolerant of IPV from their respective counterparts. Among the three community-level variables, only the mean decision score is found significant in lowering the likelihood. The thesis concludes with some policy recommendations and a proposal for future research.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013325
Subject Headings
Intimate partner violence, Bangladesh, Women
Format
Document (PDF)
Detection of multiple change-points in hazard models
Title
Detection of multiple change-points in hazard models.
Creator
Zhang, Wei, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Change-point detection in hazard rate function is an important research topic in survival analysis. In this dissertation, we firstly review existing methods for single change-point detection in piecewise exponential hazard model. Then we consider the problem of estimating the change point in the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one step...
Show moreChange-point detection in hazard rate function is an important research topic in survival analysis. In this dissertation, we firstly review existing methods for single change-point detection in piecewise exponential hazard model. Then we consider the problem of estimating the change point in the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one step further, we propose an counting process based and least squares based change-point detection algorithm. For single change-point case, consistency results are obtained. We then consider the detection of multiple change-points in the presence of long-term survivors via maximum likelihood based and counting process based method. Last but not least, we use a weighted least squares based and counting process based method for detection of multiple change-points with long-term survivors and covariates. For multiple change-points detection, simulation studies show good performances of our estimators under various parameters settings for both methods. All methods are applied to real data analyses.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004173
Subject Headings
Problem solving--Data processing., Process control--Statistical methods., Point processes., Mathematical statistics., Failure time data analysis--Data processing., Survival analysis (Biometry)--Data processing.
Format
Document (PDF)
Various Approaches on Parameter Estimation in Mixture and Non-Mixture Cure Models
Title
Various Approaches on Parameter Estimation in Mixture and Non-Mixture Cure Models.
Creator
Kutal, Durga Hari, Qian, Lianfen, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Analyzing life-time data with long-term survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or long-term survivors. In order to include the propor- tion of cure subjects, mixture and non-mixture cure models are considered. In this dissertation, we utilize both maximum likelihood and Bayesian methods to estimate model parameters. Simulation studies are carried out to verify the nite sample per-...
Show moreAnalyzing life-time data with long-term survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or long-term survivors. In order to include the propor- tion of cure subjects, mixture and non-mixture cure models are considered. In this dissertation, we utilize both maximum likelihood and Bayesian methods to estimate model parameters. Simulation studies are carried out to verify the nite sample per- formance of the estimation methods. Real data analyses are reported to illustrate the goodness-of- t via Fr echet, Weibull and Exponentiated Exponential susceptible distributions. Among the three parametric susceptible distributions, Fr echet is the most promising. Next, we extend the non-mixture cure model to include a change point in a covariate for right censored data. The smoothed likelihood approach is used to address the problem of a log-likelihood function which is not di erentiable with respect to the change point. The simulation study is based on the non-mixture change point cure model with an exponential distribution for the susceptible subjects. The simulation results revealed a convincing performance of the proposed method of estimation.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013083
Subject Headings
Survival Analysis., Bayesian statistical decision theory., Parameter estimation., Weibull distribution.
Format
Document (PDF)