Dissertations / Theses: 'Logistic regression analysis'

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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.

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Olsén, Johan. "Logistic regression modelling for STHR analysis." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148971.

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Coronary artery heart disease (CAD) is a common condition which can impair the quality of life and lead to cardiac infarctions. Traditional criteria during exercise tests are good but far from perfect. A lot of patients with inconclusive tests are referred to radiological examinations. By finding better evaluation criteria during the exercise test we can save a lot of money and let the patients avoid unnecessary examinations. Computers record amounts of numerical data during the exercise test. In this retrospective study 267 patients with inconclusive exercise test and performed radiological examinations were included. The purpose was to use clinical considerations as-well as mathematical statistics to be able to find new diagnostic criteria. We created a few new parameters and evaluated them together with previously used parameters. For women we found some interesting univariable results where new parameters discriminated better than the formerly used. However, the number of females with observed CAD was small (14) which made it impossible to obtain strong significance. For men we computed a multivariable model, using logistic regression, which discriminates way better than the traditional parameters for these patients. The area under the ROC curve was 0:90 (95 % CI: 0.83-0.97) which is excellent to outstanding discrimination in a group initially included due to their inconclusive results. If the model can be proved to hold for another population it could contribute a lot to the diagnostics of this common medical conditions

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Hu, ChungLynn. "Nonignorable nonresponse in the logistic regression analysis /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.

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Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able to determine the risk profile. The analysis is based on survey data from respondents living in Sweden. The main findings are that variables such as income, consumption rate, experience of a financial bear market, and various psychometric variables are significant in determining a financial risk profile.
I samband med en ökad automatiseringstrend har digital investeringsrådgivning dykt upp som ett nytt fenomen. Av central betydelse är tjänstens förmåga att bedöma en investerares förmåga till att bära finansiell risk. Logistik regression tillämpas för att bedöma en icke- professionell investerares vilja att bära finansiell risk. Målet med uppsatsen är således att identifiera ett antal faktorer med signifikant förmåga till att bedöma en icke-professionell investerares riskprofil. Med andra ord, så syftar denna uppsats till att studera förmågan hos ett antal socioekonomiska- och psykometriska variabler. För att därigenom utveckla en prediktiv modell som kan skatta en individs finansiella riskprofil. Analysen genomförs med hjälp av en enkätstudie hos respondenter bosatta i Sverige. Den huvudsakliga slutsatsen är att en individs inkomst, konsumtionstakt, tidigare erfarenheter av abnorma marknadsförhållanden, och diverse psykometriska komponenter besitter en betydande förmåga till att avgöra en individs finansiella risktolerans

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Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.

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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases

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Pan, Tianshu. "Using the multivariate multilevel logistic regression model to detect DIF a comparison with HGLM and logistic regression DIF detection methods /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008.
Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.

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McGlothlin, Anna E. Stamey James D. Seaman John Weldon. "Logistic regression with misclassified response and covariate measurement error a Bayesian approach /." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5101.

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Lindroth, Henriksson Amelia, and Simon Koller. "Logistic Regression Analysis of Patent Approval Rate in Sweden." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230143.

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This thesis was conducted to investigate what factors impact the outcome of a patent application for the Swedish market. The method used was logistic regression and the data was extracted from the database of The Swedish Patent and Registration Offi ce, PRV. The analysis in this thesis started with 47 covariates, including the 35 IPO technical fields, resulting in a model consisting of five covariates. The most important covariates were determined to be the number of notices issued by PRV, whether or not a patent attorney was used and applicant type. The number of notices had a positive impact on the probability of the success of a patent application. Being a company and hiring a patent attorney also increase the chances of the patent being granted. The derived final model showed a high predictive ability and provides insight of significant factors of a successful patent application.
Denna avhandling utfördes för att undersöka vilka faktorer som påverkar utfallen av patentansökningar för den svenska marknaden. Metoden som användes var logistisk re- gression, och datan är hämtad från Patent- och Registreringsverkets, PRVs, databas. Analysen i avhandlingen utfördes på 47 kovariat, inklusive IPOs 35 teknikområden. Detta resulterade i en modell som består av fem kovariat. De viktigaste kovariaten beräknades vara antalet skick mellan PRV och sökanden, huruvida man nyttjat sig av ett patentombud eller ej samt om sökande var en privatperson eller juridisk person. Antalet skick hade en positiv påverkan på sannolikheten för en godkänd patentansökan. Företag och sökanden som använde sig av ett patentombud hade också högre sannolikhet att få sina patent godkända. Den härledda slutgiltiga modellen visade sig ha hög förutsägningsförmåga och ger en insikt om signifikanta faktorer för en framgångsrik patentansökan.

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Heise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.

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In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index. Most experimenters will be less familiar with the Gumbel bivariate logistic regression model than with the univariate logistic regression models which comprise its marginals. Therefore, the optimal designs based on the Gumbel model are evaluated based on univariate logistic regression D-efficiencies; conversely, designs derived from the univariate logistic regression model are evaluated with respect to the Gumbel optimality criteria. Further practical considerations motivate an exploration of designs providing a maximum compromise between the three Gumbel-based criteria D, Q and T. Finally, 5-point designs which can be generated by fitted equations are proposed as a practical option for experimental use.
Ph. D.

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Jin, Yi. "Regression Analysis of University Giving Data." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/1.

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This project analyzed the giving data of Worcester Polytechnic Institute's alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due to the nature of the data, multiple imputation was performed on the missing information to validate generalization of the models to a broader population. Concepts from the field of direct and database marketing, like "score" and "lift", were also introduced in this report.

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Guo, Ruijuan. "Sample comparisons using microarrays: - Application of False Discovery Rate and quadratic logistic regression." Digital WPI, 2008. https://digitalcommons.wpi.edu/etd-theses/28.

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In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.

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Simmons, Carol Ivy. "A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions." Thesis, Capella University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10289773.

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This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals control i.e., length of hospital stay and reimbursement processes. The goal of the study was to explore the impact of multiple factors in relation to the phenomenon of psychiatric readmissions. Dynamic Systems Theory (1994) was used as a theoretical foundation to understand the complexities associated with psychiatric readmissions. The study utilized state archival data provided by the Maryland Health Services Cost Review Commission; an agency charged with collecting statewide hospital data on hospital admissions.

A quasi experimental study was conducted using a logistic regression design to answer the research question: When taken together do age, sex, ethnicity, diagnosis, insurance type, admission status and length of stay predict psychiatric readmission? This researcher predicted that the null hypothesis will be rejected. The sample included a large state-wide data set of over 130,000 individuals who fell under the criteria of being over the age of 18 when readmitted for psychiatric care in Maryland in 2015. The research methodology includes a logistic regression research design, exploring multiple factors, simultaneously, that impact psychiatric readmissions.

The results of the study indicate that length of stay is the most important factor impacting psychiatric readmissions. The second most important factor associated with psychiatric readmission, is a psychiatric readmission within 30 days. Medicare and Medicaid were also found to be significant factors associated with psychiatric readmission. Additionally, affective disorders were found to be the primary diagnosis associated with psychiatric readmissions. Lastly, individuals at greatest risk for psychiatric readmissions are between the age of 18-39, are non-Hispanic, are enrolled in Medicare, most likely to be disabled, are diagnosed with an affective disorder and have had a previous psychiatric readmission.

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Adnan, Arisman. "Analysis of taste-panel data using ANOVA and ordinal logistic regression." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402150.

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Chen, Wei. "Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5923.

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In this paper, a rheumatoid arthritis (RA) medicine clinical dataset with an ordinal response is selected to study this new medicine. In the dataset, there are four features, sex, age,treatment, and preliminary. Sex is a binary categorical variable with 1 indicates male, and 0 indicates female. Age is the numerical age of the patients. And treatment is a binary categorical variable with 1 indicates has RA, and 0 indicates does not have RA. And preliminary is a five class categorical variable indicates the patient’s RA severity status before taking the medication. The response Y is 5 class ordinal variable shows the severity of patient’s RA severity after taking the medication. The primary aim of this study is to determine what factors play a significant role in determine the response after taking the medicine. First, cumulative logistic regression is applied to the dataset to examine the effect of various factors on ordinal response. Secondly, the ordinal response is categorized into two classes. Then logistic regression is conducted to the RA dataset to see if the variable selection would be different. Moreover, the shrinkage methods, elastic net and lasso are used to make a variable selection on the RA dataset of two-class response for the purpose of adding penalization to increase the model’s robustness.The four model results were compared at the end of the paper. From the comparison result, logistic regression has a better performance on variable selection than the other three approaches based on P-value.

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Wu, Songfei. "A combination procedure of universal kriging and logistic regression a thesis presented to the faculty of the Graduate School, Tennessee Technological University /." Click to access online, 2008. http://proquest.umi.com/pqdweb?index=31&sid=1&srchmode=1&vinst=PROD&fmt=6&startpage=-1&clientid=28564&vname=PQD&RQT=309&did=1679675411&scaling=FULL&ts=1251312326&vtype=PQD&rqt=309&TS=1251312380&clientId=28564.

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Whitmore, Marjorie Lee Threet. "A Comparison of Two Differential Item Functioning Detection Methods: Logistic Regression and an Analysis of Variance Approach Using Rasch Estimation." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc278366/.

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Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, and each experiment was replicated 100 times. For both methods and all DIF types, a test length of 20 was sufficient for satisfactory DIF detection. The detection rate increased significantly with sample size for each method. With the ANOVA DIF method and uniform DIF, there was a difference in detection rates between discrimination parameter types, which favored varying discrimination and decreased with increased sample size. The detection rate of non-uniform DIF using the ANOVA DIF method was higher with fixed discrimination parameters than with varying discrimination parameters when relative underlying ability was unequal. In the combination DIF case, there was a three-way interaction among the experimental factors discrimination type, relative ability, and sample size for both detection methods. The error rate for the ANOVA DIF detection method decreased as test length increased and increased as sample size increased. For both methods, the error rate was slightly higher with varying discrimination parameters than with fixed. For logistic regression, the error rate increased with sample size when relative underlying ability was unequal between groups. The logistic regression method detected uniform and non-uniform DIF at a higher rate than the ANOVA DIF method. Because the type of DIF present in real data is rarely known, the logistic regression method is recommended for most cases.

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Alexander, Erika D. "Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4242/.

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The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.

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Junek, William N. "Forecasting Volcanic Activity Using An Event Tree Analysis System and Logistic Regression." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5333.

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Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree's decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system.
ID: 031001329; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: W. Linwood Jones.; Title from PDF title page (viewed April 8, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references (p. 314-324).
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering

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Weng, Chin-Fang. "Fixed versus mixed parameterization in logistic regression models application to meta-analysis /." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8985.

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Thesis (M.A.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Crixell, JoAnna Christine Seaman John Weldon Stamey James D. "Logistic regression with covariate measurement error in an adaptive design a Bayesian approach /." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5229.

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Louw, Nelmarie. "Aspects of the pre- and post-selection classification performance of discriminant analysis and logistic regression." Thesis, Stellenbosch : Stellenbosch University, 1997. http://hdl.handle.net/10019.1/55402.

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Thesis (PhD)--Stellenbosch University, 1997.
One copy microfiche.
ENGLISH ABSTRACT: Discriminani analysis and logistic regression are techniques that can be used to classify entities of unknown origin into one of a number of groups. However, the underlying models and assumptions for application of the two techniques differ. In this study, the two techniques are compared with respect to classification of entities. Firstly, the two techniques were compared in situations where no data dependent variable selection took place. Several underlying distributions were studied: the normal distribution, the double exponential distribution and the lognormal distribution. The number of variables, sample sizes from the different groups and the correlation structure between the variables were varied to' obtain a large number of different configurations. .The cases of two and three groups were studied. The most important conclusions are: "for normal and double' exponential data linear discriminant analysis outperforms logistic regression, especially in cases where the ratio of the number of variables to the total sample size is large. For lognormal data, logistic regression should be preferred, except in cases where the ratio of the number of variables to the total sample size is large. " Variable selection is frequently the first step in statistical analyses. A large number of potenti8.Ily important variables are observed, and an optimal subset has to be selected for use in further analyses. Despite the fact that variable selection is often used, the influence of a selection step on further analyses of the same data, is often completely ignored. An important aim of this study was to develop new selection techniques for use in discriminant analysis and logistic regression. New estimators of the postselection error rate were also developed. A new selection technique, cross model validation (CMV) that can be applied both in discriminant analysis and logistic regression, was developed. ."This technique combines the selection of variables and the estimation of the post-selection error rate. It provides a method to determine the optimal model dimension, to select the variables for the final model and to estimate the post-selection error rate of the discriminant rule. An extensive Monte Carlo simulation study comparing the CMV technique to existing procedures in the literature, was undertaken. In general, this technique outperformed the other methods, especially with respect to the accuracy of estimating the post-selection error rate. Finally, pre-test type variable selection was considered. A pre-test estimation procedure was adapted for use as selection technique in linear discriminant analysis. In a simulation study, this technique was compared to CMV, and was found to perform well, especially with respect to correct selection. However, this technique is only valid for uncorrelated normal variables, and its applicability is therefore limited. A numerically intensive approach was used throughout the study, since the problems that were investigated are not amenable to an analytical approach.
AFRIKAANSE OPSOMMING: Lineere diskriminantanaliseen logistiese regressie is tegnieke wat gebruik kan word vir die Idassifikasie van items van onbekende oorsprong in een van 'n aantal groepe. Die agterliggende modelle en aannames vir die gebruik van die twee tegnieke is egter verskillend. In die studie is die twee tegnieke vergelyk ten opsigte van k1assifikasievan items. Eerstens is die twee tegnieke vergelyk in 'n apset waar daar geen data-afhanklike seleksie van veranderlikes plaasvind me. Verskeie onderliggende verdelings is bestudeer: die normaalverdeling, die dubbeleksponensiaal-verdeling,en die lognormaal verdeling. Die aantal veranderlikes, steekproefgroottes uit die onderskeie groepe en die korrelasiestruktuur tussen die veranderlikes is gevarieer om 'n groot aantal konfigurasies te verkry. Die geval van twee en drie groepe is bestudeer. Die belangrikste gevolgtrekkings wat op grond van die studie gemaak kan word is: vir normaal en dubbeleksponensiaal data vaar lineere diskriminantanalise beter as logistiese regressie, veral in gevalle waar die. verhouding van die aantal veranderlikes tot die totale steekproefgrootte groot is. In die geval van data uit 'n lognormaalverdeling, hehoort logistiese regressie die metode van keuse te wees, tensy die verhouding van die aantal veranderlikes tot die totale steekproefgrootte groot is. Veranderlike seleksie is dikwels die eerste stap in statistiese ontledings. 'n Groot aantal potensieel belangrike veranderlikes word waargeneem, en 'n subversamelingwat optimaal is, word gekies om in die verdere ontledings te gebruik. Ten spyte van die feit dat veranderlike seleksie dikwels gebruik word, word die invloed wat 'n seleksie-stap op verdere ontledings van dieselfde data. het, dikwels heeltemal geYgnoreer.'n Belangrike doelwit van die studie was om nuwe seleksietegniekete ontwikkel wat gebruik kan word in diskriminantanalise en logistiese regressie. Verder is ook aandag gegee aan ontwikkeling van beramers van die foutkoers van 'n diskriminantfunksie wat met geselekteerde veranderlikes gevorm word. 'n Nuwe seleksietegniek, kruis-model validasie (KMV) wat gebruik kan word vir die seleksie van veranderlikes in beide diskriminantanalise en logistiese regressie is ontwikkel. Hierdie tegniek hanteer die seleksie van veranderlikes en die beraming van die na-seleksie foutkoers in een stap, en verskaf 'n metode om die optimale modeldimensiete bepaal, die veranderlikes wat in die model bevat moet word te kies, en ook die na-seleksie foutkoers van die diskriminantfunksie te beraam. 'n Uitgebreide simulasiestudie waarin die voorgestelde KMV-tegniek met ander prosedures in die Iiteratuur. vergelyk is, is vir beide diskriminantanaliseen logistiese regressie ondemeem. In die algemeen het hierdie tegniek beter gevaar as die ander metodes wat beskou is, veral ten opsigte van die akkuraatheid waarmee die na-seleksie foutkoers beraam word. Ten slotte is daar ook aandag gegee aan voor-toets tipeseleksie. 'n Tegniek is ontwikkel wat gebruik maak van 'nvoor-toets berarningsmetode om veranderlikes vir insluiting in 'n lineere diskriminantfunksie te selekteer. Die tegniek ISin 'n simulasiestudie met die KMV-tegniek vergelyk, en vaar baie goed, veral t.o.v. korrekte seleksie. Hierdie tegniek is egter slegs geldig vir ongekorreleerde normaalveranderlikes, wat die gebruik darvan beperk. 'n Numeries intensiewe benadering is deurgaans in die studie gebruik. Dit is genoodsaak deur die feit dat die probleme wat ondersoek is, nie deur middel van 'n analitiese benadering hanteer kan word nie.

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Guo, Ruijuan. "Sample comparisons using microarrays -- application of false discovery rate and quadratic logistic regression." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-010808-173747/.

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Maxwell, Kori Lloyd Hugh. "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/67.

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Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

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Geroukis, Asterios, and Erik Brorson. "Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243289.

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In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio – using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of risk. The results shows that the logistic regression outperforms the discriminant analysis in classifying all of the groups except for the middle one. We conclude that this is in line with previous studies.

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Hardin, Patrik, and Sam Tabari. "Modelling Non-life Insurance Policyholder Price Sensitivity : A Statistical Analysis Performed with Logistic Regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209773.

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This bachelor thesis within mathematical statistics studies the possibility of modelling the renewal probability for commercial non-life insurance policyholders. The project was carried out in collaboration with the non-life insurance company If P&C Insurance Ltd. at their headquarters in Stockholm, Sweden. The paper includes an introduction to underlying concepts within insurance and mathematics and a detailed review of the analytical process followed by a discussion and conclusions. The first stages of the project were the initial collection and processing of explanatory insurance data and the development of a logistic regression model for policy renewal. An initial model was built and modern methods of mathematics and statistics were applied in order obtain a final model consisting of 9 significant characteristics. The regression model had a predictive power of 61%. This suggests that it to a certain degree is possible to predict the renewal probability of non-life insurance policyholders based on their characteristics. The results from the final model were ultimately translated into a measure of price sensitivity which can be implemented in both pricing models and CRM systems. We believe that price sensitivity analysis, if done correctly, is a natural step in improving the current pricing models in the insurance industry and this project provides a foundation for further research in this area.
Detta kandidatexamensarbete inom matematisk statistik undersöker möjligheten att modellera förnyelsegraden för kommersiella skadeförsärkringskunder. Arbetet utfördes i samarbete med If Skadeförsäkring vid huvudkontoret i Stockholm, Sverige. Uppsatsen innehåller en introduktion till underliggande koncept inom försäkring och matematik samt en utförlig översikt över projektets analytiska process, följt av en diskussion och slutsatser. De huvudsakliga delarna av projektet var insamling och bearbetning av förklarande försäkringsdata samt utvecklandet och tolkningen av en logistisk regressionsmodell för förnyelsegrad. En första modell byggdes och moderna metoder inom matematik och statistik utfördes för att erhålla en slutgiltig regressionsmodell uppbyggd av 9 signifikanta kundkaraktäristika. Regressionsmodellen hade en förklaringsgrad av 61% vilket pekar på att det till en viss grad är möjligt att förklara förnyelsegraden hos försäkringskunder utifrån dessa karaktäristika. Resultaten från den slutgiltiga modellen översattes slutligen till ett priskänslighetsmått vilket möjliggjorde implementering i prissättningsmodeller samt CRM-system. Vi anser att priskänslighetsanalys, om korrekt genomfört, är ett naturligt steg i utvecklingen av dagens prissättningsmodeller inom försäkringsbranschen och detta projekt lägger en grund för fortsatta studier inom detta område.

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Sturm, Pamela S. "Knowing when a higher education institution is in trouble." Huntington, WV : [Marshall University Libraries], 2005. http://www.marshall.edu/etd/descript.asp?ref=583.

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Vizcain, Dorian Charles. "Investigating the Hispanic/Latino Male Dropout Phenomenon: Using Logistic Regression and Survival Analysis." [Tampa, Fla] : University of South Florida, 2005. http://purl.fcla.edu/usf/dc/et/SFE0001322.

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Welch, Catherine E. "Factors Affecting Postsecondary Enrollment among Vermont High School Graduates| A Logistic Regression Analysis." Thesis, New England College, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13859163.

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The State of Vermont has long had one of the highest high school graduation rates in New England, hovering around 87.8% with a lagging college enrollment rate of 52.3% at any 2- or 4-year postsecondary institution in the country (New England Secondary School Consortium, 2015). This research explored the factors that have the greatest effect on the college enrollment patterns of Vermont high school graduates. Specifically, this study explored the relationship between the following factors and 2- and 4-year college enrollment: (a) academic preparation, (b) access to college information, (c) early career exploration and education planning, (d) gender, (e) grade point average, (f) parent educational attainment, (g) parental expectations, (h) student location, and (i) student perception of affordability.

This descriptive, correlational quantitative study used binomial logistic regression to determine which of the factors listed in the preceding section had the greatest impact on the college enrollment patterns of Vermont high school graduates. The dataset for this research was the Class of 2014 Senior Survey from the Vermont Student Assistance Corporation, administered to all students graduating from Vermont high schools in 2014. This research looks to inform work currently being done at the state level to raise the number of adults living in Vermont with a postsecondary credential to 70% by the year 2025

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VENUGOPALAN, ARAVIND. "STATISTICAL ANALYSIS OF POSTERIOR FOSSA SURGERIES FOR TRIGEMINAL NEURALGIA USING LOGISTIC REGRESSION MODELS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155831511.

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Spence, Maria A. Stancil. "Successful vocational rehabilitation for persons with significant mental disabilities : a logistic regression analysis /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488196234910504.

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Yanik, Todd E. "Detection of erroneous payments utilizing supervised and utilizing supervised and unsupervised data mining techniques." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FYanik.pdf.

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CRISANTI, MARK. "THE PREDICTIVE ACCURACY OF BOOSTED CLASSIFICATION TREES RELATIVE TO DISCRIMINANT ANALYSIS AND LOGISTIC REGRESSION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178566287.

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Lee, Michelle Oi San. "Sample size calculation for testing an interaction effect in a logistic regression under measurement error model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20LEE.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.

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Whitten, Tyson. "Defining and Measuring Persistent Offending." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/378078.

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BACKGROUND Persistent offending has been a topic of interest in the criminal justice literature for some time. Despite its popularity, the concept remains without a consistent or agreed upon definition. For example, persistent offending has been variably defined as offending before and during adulthood (e.g., Bergman & Andershed, 2009), frequent offending (e.g., McGloin & Stickle, 2011), or an early age of first offence (e.g. Hay & Forrest, 2009). In turn, inconsistent methods have been used to operationalise and identify persistent offenders, such as offending before and after the age of 21 (Farrington, Ttofi, & Coid, 2009), the five percent most frequent offenders (Piquero & Lawton, 2002), and multiple offences committed before the age of 14 (Hagell & Newburn, 1994). The definitional inconsistency surrounding the concept of persistent offending poses a significant threat to the generalisability of research, accuracy of theory, and efficacy of policy and interventions. Although previous authors have highlighted their concerns regarding the methodological inconsistencies pertaining to research on persistent offending (Hagell & Newburn, 1994; Piquero, 2009), these concerns seem to have fallen on deaf ears. AIMS This dissertation argues that the use of inconsistent definitions and operationalisations of persistent offending are contributing to the inconsistent findings and competing explanations on the phenomenon. Therefore, more conceptual discussions and empirical observations drawing attention to the ramifications of this issue, as well as methods for rectifying the problem, are needed. Through a series of published and unpublished papers, this dissertation attempts to meet this need by: (1) Highlighting the prevalence of inconsistent definitions, operationalisations, and measures of persistent offending in the literature, and the consequent need for consistency; (2) Empirically demonstrating the flaws associated with these inconsistencies, and; (3) Proposing how to best define, operationalise, and measure persistent offending. The underlying position of this dissertation is that conceptually, persistent offending is best defined and measured by the duration of the criminal career. The arguments and empirical findings in this dissertation support this premise. DATA AND ANALYSES This dissertation uses data from the Cambridge Study in Delinquent Development (CSDD). The CSDD is a longitudinal, population-based study that, to date, has observed the development of offending behaviours in 411 South London males from the age of eight to 56. Boys were interviewed in school at ages eight, ten, and 14 years. Conviction data was recorded annually from the age of 10 to 56. All offences leading to a conviction, excluding minor offences such as traffic infractions and public intoxication, were included in the analyses. The vast majority of men (91 percent) were at risk of conviction at 56 years of age. Chi-square, multiple, and logistic regression analyses were used to examine the association between childhood risk factors and conviction frequency, criminal career duration, and different offending pathways. Descriptive statistics and odds ratios were conducted in order to examine the overlap in the number of offenders identified by different operationalisations of persistent offending. Finally, Pearson’s and partial correlation were used to examine the relationship between criminal career duration, conviction frequency, and age of first conviction. RESULTS Five key findings are reported in this dissertation. First, reviews of the published literature indicated that many of competing empirical findings can be attributed to the use of different definitions and operationalisations of persistent offending. Second, competing measures of persistent offending (i.e., criminal career duration and conviction frequency) are associated with different types and numbers of childhood risk factors. Indeed, not only did offenders with the longest criminal careers have fewer childhood risk factors than offenders with the most convictions, but the childhood risk factors associated with these offenders did not differ to those experienced by one-time offenders. Third, depending on the key measure used, different operationalisations of persistent offending generally identify vastly different offenders as persistent. Fourth, when controlling for offence frequency, onset age is not associated with criminal career duration. Finally, persistent offenders identified by the duration of the criminal career tend to have the longest criminal careers, a more normative age of onset, and vary in their conviction rates. CONCLUSION The collective results of this dissertation support the idea that persistent offending should logically and consistently be defined and measured by the duration of the criminal career. More so, it is proposed that persistent offending should be defined as a criminal career that exceeds the average duration for a criminal career in a population or offender based sample. Nonetheless, a fundamental limitation of proposing a specific definition of persistent offending is that, due to the concepts ambiguity, there are no clear right answers. It may therefore be some time before there is accord on how to define and identify this phenomenon. Nevertheless, it is hoped that, if nothing else, the arguments and findings in this dissertation will spur more scholarly discussions, and help pave the way towards establishing a consistently used definition of persistent offending.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Crim & Crim Justice
Arts, Education and Law
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Cronstedt, Axel, and Rebecca Andersson. "Readjusting Historical Credit Ratings : using Ordered Logistic Regression and Principal ComponentAnalysis." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-148567.

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Readjusting Historical Credit Ratings using Ordered Logistic Re-gression and Principal Component Analysis The introduction of the Basel II Accord as a regulatory document for creditrisk presented new concepts of credit risk management and credit risk mea-surements, such as enabling international banks to use internal estimates ofprobability of default (PD), exposure at default (EAD) and loss given default(LGD). These three measurements is the foundation of the regulatory capitalcalculations and are all in turn based on the bank’s internal credit ratings. Ithas hence been of increasing importance to build sound credit rating modelsthat possess the capability to provide accurate measurements of the credit riskof borrowers. These statistical models are usually based on empirical data andthe goodness-of-fit of the model is mainly depending on the quality and sta-tistical significance of the data. Therefore, one of the most important aspectsof credit rating modeling is to have a sufficient number of observations to bestatistically reliable, making the success of a rating model heavily dependenton the data collection and development state.The main purpose of this project is to, in a simple but efficient way, createa longer time series of homogeneous data by readjusting the historical creditrating data of one of Svenska Handelsbanken AB’s credit portfolios. Thisreadjustment is done by developing ordered logistic regression models thatare using independent variables consisting of macro economic data in separateways. One model uses macro economic variables compiled into principal com-ponents, generated through a Principal Component Analysis while all othermodels uses the same macro economic variables separately in different com-binations. The models will be tested to evaluate their ability to readjust theportfolio as well as their predictive capabilities.
Justering av historiska kreditbetyg med hjälp av ordinal logistiskregression och principialkomponentsanalys När Basel II implementerades introducerades även nya riktlinjer för finan-siella instituts riskhantering och beräkning av kreditrisk, så som möjlighetenför banker att använda interna beräkningar av Probability of Default (PD),Exposure at Default (EAD) och Loss Given Default (LGD), som tillsammansgrundar sig i varje låntagares sannoliket för fallissemang. Dessa tre mått ut-gör grunden för beräkningen av de kapitaltäckningskrav som banker förväntasuppfylla och baseras i sin tur på bankernas interna kreditratingsystem. Detär därmed av stor vikt för banker att bygga stabila kreditratingmodeller medkapacitet att generera pålitliga beräkningar av motparternas kreditrisk. Dessamodeller är vanligtvis baserade på empirisk data och modellens goodness-of-fit,eller passning till datat, beror till stor del på kvalitén och den statistiska sig-nifikansen hos det data som står till förfogande. Därför är en av de viktigasteaspekterna för kreditratingsmodeller att ha tillräckligt många observationeratt träna modellen på, vilket gör modellens utvecklingsskede samt mängdendata avgörande för modellens framgång.Huvudsyftet med detta projekt är att, på ett enkelt och effektivt sätt, skapaen längre, homogen tidsserie genom att justera historisk kreditratingdata i enportfölj med företagslån tillhandahållen av Svenska Handelsbanken AB. Jus-teringen görs genom att utveckla olika ordinala logistiska regressionsmodellermed beroende variabler bestående av makroekonomiska variabler, på olikasätt. En av modellerna använder makroekonomiska variabler i form av princi-palkomponenter skapade med hjälp av en principialkomponentsanalys, medande andra modelelrna använder de makroekonomiska variablerna enskilt i olikakombinationer. Modellerna testas för att utvärdera både deras förmåga attjustera portföljens historiska kreditratings samt för att göra prediktioner.

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Stevenson, Clint Wesley. "A logistic regression analysis of utah colleges exit poll response rates using SAS software /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1578.pdf.

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Aranda, Diana Ixchel. "Historical Analysis of Recreational Beach Enterococci Levels; Using Logistic Regression as an Advisory Tool." NSUWorks, 2013. http://nsuworks.nova.edu/occ_stuetd/182.

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Enterococci levels are measured to assess water safety in recreational beaches through a state surveillance program. This surveillance informs the public of beach safety, yet the sampling methodology is limited to only making an advisory posting one sample at a time. This methodology poses a challenge for managers such as: 24 hour advisory waiting period, untested days and extreme variability of enterococci levels in the environment. Therefore, there is a need to integrate adaptive management methodologies that can assist managers to proactively assess beach water safety. This study explored the utility of a historical analysis and logistic regression modeling as a method and as an advisory tool. The analysis utilized 10 years of enterococci surveillance data (7,422 samples) from 15 sub-tropical beaches in Miami-Dade County, Florida. It was determined that Miami beaches have historical low enterococci exceedance counts (3% of total data), that there are some beaches that are more propense to higher exceedance counts than others and that the wet season overall did not readily appear to affect exceedances counts. The logistic regression model utilized an exceedance/ non-exceedance dichotomy and spatial, temporal and annual variables. The model indicated that the overall range of probability of having an exceedance for the sampled beaches under each variable was less than 10%. The ability to use this model and get probability results showed that logistic regression is an accurate statistical tool that provides the historical probabilities of an exceedance on a beach and can complement a random sampling methodology. Furthermore it’s a simple and inexpensive methodology that provides the ability to categorize and recognize patterns estimating the surveillance-managed sample sites probabilities that provides foresight as to where to focus resources in order to reduce risk and facilitating beach management. Through the use of a historical analysis and a logistic regression model, it is possible to address dynamic recreational beach environments with a large-scale view and in a historically comprehensive manner, instead of only making management choices sample by sample.

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Honarvar, Pauline. "A spatial approach to mineral potential modelling using decision tree and logistic regression analysis /." Internet access available to MUN users only, 2001. http://collections.mun.ca/u?/theses,51228.

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Stevenson, Clint W. "A Logistic Regression Analysis of Utah Colleges Exit Poll Response Rates Using SAS Software." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1116.

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In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. An interviewer's prior retail sales experience is associated with whether a voter will decide to respond to a questionnaire or not. The only exogenous factor that is associated with voter response is whether the interview occurred in the morning or afternoon.

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Oates, Krystle S. "A logistic regression analysis of score sending and college matching among high school students." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1994.

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College decisions are often the result of a variety of influences related to student background characteristics, academic characteristics, college preferences and college aspirations. College counselors recommend that students choose a variety of schools, especially schools where the general student body matches the academic achievement of students. These types of schools are generally referred to as match schools. This thesis examined the initial college decisions of high school students in a large Midwestern state, who were an academic match for selective and highly selective schools by observing the student characteristics that were most influential in predicting college matching for students’ initial first choice institution. This thesis also observed college enrollment among students who chose a match school as their first choice institution, college matching over a time period from 1992 to 2013, and college matching after the implementation of a state initiative designed to help students apply for college. Logistic regression along with descriptive statistics were used as the primary analyses for college matching. Results from these analyses showed that students belonging to underrepresented minority groups had odds of college matching for their first choice institution that were significantly greater than white students. Students whose parents earned at least a bachelor’s degree had odds that were significantly greater than students whose parents had not earned a bachelor’s degree. Also, students whose coursework included calculus and physics, and students who planned to earn a graduate degree had significantly greater odds of matching on their first choice institution than students who were not a part of these respective groups. Among students in the sample who chose a match school for their first choice institution, students who had at least one parent earn up to a bachelor’s degree were significantly more likely to enroll in a match school. Also, the percentage of students at a single high school who were eligible for free and reduced lunch were negatively associated with the odds of students enrolling in a match school. To observe score sending among students to their first choice institution over time an additional variable, “year” was added to the logistic regression model to compare the years of 2000, 2008 and 2013 to 1992. The results of this logistic regression analysis showed that students’ odds of choosing a match school for their first choice institution were significantly lower in 2008 and 2013 than in 1992. College matching for students who attended high schools serviced by the state initiative were compared using the percentage differences in college matching before and after the implementation of the program. However, results could not be interpreted with certainty due to the small size of the sample.

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Wang, Junhua. "Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/103.

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We considered the problem of predicting student retention using logistic regression when the most important covariates such as the college variables are latent, but the network structure is known. This network structure specifies the relationship between pre-college to college variables and then from college to student retention variables. Based on this structure, we developed three estimators, examined their large-sample properties, and evaluated their empirical efficiencies using WKU student retention database. Results show that while the hat estimator is expected to be most efficient, the tilde estimator was shown to be more efficient than the check method. This increased efficiency suggests that utilizing the network information can improve our predictions.

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MATYATIM, Rosliza. "The Classification Model for Corporate Failures in Malaysia." Graduate School of International Development, Nagoya University, 2006. http://hdl.handle.net/2237/7314.

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VACCARELLA, SALVATORE. "A multilevel logistic regression model for the analyses of concurrent Human papillomavirus (HPV) infections." Doctoral thesis, Università degli Studi di Milano, 2007. http://hdl.handle.net/2434/33629.

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Human Papillomavirus (HPV) is a sexually transmitted infection that causes cervical cancer. A nested three-level logistic regression model was introduced in order to investigate whether, in the IARC HPV prevalence surveys, co-infection with different HPV types occurs more or less frequently than expected if the infections are independent from one another. Two random effects, at individual and study-area level, were specified, while the fixed-effect covariates at individual level were age and lifetime number of sexual partners. The Best Linear Unbiased Predictors (BLUP) technique was used to estimate the random components. The predictions of the random effects at individual level are particularly important because they can be considered as a synthetic estimate of all those residual sources of individual variability, i.e., unmeasured risk factors due to sexual habits, that otherwise could not be accounted for. Individual probabilities of being positive for each HPV type are thus estimated, and the expected vs observed number of infections are compared, given the positivity for a different HPV type. Few positive associations (HPV58 with 33 being the strongest) were found in this analyses. However, the majority of HPV types, particularly the two most oncogenic types, HPV16 and 18, that are also included in the prophylactic vaccine, were not associated with one another.

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Gusnanto, Arief. "Regression on high-dimensional predictor space : with application in chemometrics and microarray data /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7140-153-9/.

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Wålinder, Andreas. "Evaluation of logistic regression and random forest classification based on prediction accuracy and metadata analysis." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-35126.

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Model selection is an important part of classification. In this thesis we study the two classification models logistic regression and random forest. They are compared and evaluated based on prediction accuracy and metadata analysis. The models were trained on 25 diverse datasets. We calculated the prediction accuracy of both models using RapidMiner. We also collected metadata for the datasets concerning number of observations, number of predictor variables and number of classes in the response variable. There is a correlation between performance of logistic regression and random forest with significant correlation of 0.60 and confidence interval [0.29 0.79]. The models appear to perform similarly across the datasets with performance more influenced by choice of dataset rather than model selection. Random forest with an average prediction accuracy of 81.66% performed better on these datasets than logistic regression with an average prediction accuracy of 73.07%. The difference is however not statistically significant with a p-value of 0.088 for Student's t-test. Multiple linear regression analysis reveals none of the analysed metadata have a significant linear relationship with logistic regression performance. The regression of logistic regression performance on metadata has a p-value of 0.66. We get similar results with random forest performance. The regression of random forest performance on metadata has a p-value of 0.89. None of the analysed metadata have a significant linear relationship with random forest performance. We conclude that the prediction accuracies of logistic regression and random forest are correlated. Random forest performed slightly better on the studied datasets but the difference is not statistically significant. The studied metadata does not appear to have a significant effect on prediction accuracy of either model.

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Reischman, Diann. "Order restricted inferences on parameters in generalized linear models with emphasis on logistic regression /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842560.

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Flor, Andrew Douglas. "Evaluating Levee Failure Susceptibility on the Mississippi River Using Logistic Regression Analysis and GPS Surveying." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1791850971&sid=8&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Mitchell, Marlon R. "Participation in adult education activities logistic regression analysis of baby boomers in the United States /." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3274281.

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Thesis (Ph.D.)--Indiana University, Instructional Systems Technology, 2007.
Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 2763. Adviser: Thomas Schwen. Title from dissertation home page (viewed Apr. 9, 2008).

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Kim, Hyun-Joo. "Model selection criteria based on Kullback information measures for Weibull, logistic, and nonlinear regression frameworks /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9988677.

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Jian, Wen. "Analysis of Longitudinal Data in the Case-Control Studies via Empirical Likelihood." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/math_theses/8.

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The case-control studies are primary tools for the study of risk factors (exposures) related to the disease interested. The case-control studies using longitudinal data are cost and time efficient when the disease is rare and assessing the exposure level of risk factors is difficult. Instead of GEE method, the method of using a prospective logistic model for analyzing case-control longitudinal data was proposed and the semiparametric inference procedure was explored by Park and Kim (2004). In this thesis, we apply an empirical likelihood ratio method to derive limiting distribution of the empirical likelihood ratio and find one likelihood-ratio based confidence region for the unknown regression parameters. Our approach does not require estimating the covariance matrices of the parameters. Moreover, the proposed confidence region is adapted to the data set and not necessarily symmetric. Thus, it reflects the nature of the underlying data and hence gives a more representative way to make inferences about the parameter of interest. We compare empirical likelihood method with normal approximation based method, simulation results show that the proposed empirical likelihood ratio method performs well in terms of coverage probability.

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Dissertations / Theses on the topic 'Logistic regression analysis'

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