Browsing by Subject "Regression analysis"
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Item A class of nonparametric estimators of regression coefficients in an autoregressive scheme(Texas Tech University, 1976-05) Kulp, Richard WayneNot availableItem A comparative study of rank estimates in linear models(Texas Tech University, 1996-12) Jordan, Heather R.NOT AVAILABLEItem A comparative study of rank tests for regression(Texas Tech University, 1995-08) Robertson, Stephen D.Five different nonparametric approaches, based on ranks, for testing hypothesis in a general linear regression model will be considered, and comparisons will be made between the performances of each of these five statistics, under different hypotheses and conditions. Comparisons will also be made between these nonparametric approaches and the classical parametric F test in order to determine any possible significant differences and similarities between these theoretically different methods. Some versions of these comparisons for some of these nonparametric approaches have been made in Hettmansperger and McKean[l], but this new study considers cases not studied in their study and considers some new ideas that have not yet been presented.Item A cross-national analysis of political violence: a model specification and its empirical test(Texas Tech University, 1979-12) Yough, Syng NamNot availableItem An adjustment procedure for multicollinearity in multiple regression analysis(Texas Tech University, 1973-12) Cook, James R.Not availableItem Charter schools: a study of determinants of growth(Texas Tech University, 2004-05) Sikes, Vanessa MariahAs one of the latest reform movements in education, charter schools provide a complex topic for review that can be undertaken from numerous angles. This study looks at which factors determine the number of charter schools in a given state. A background review of six variables hypothesized to influence growth in numbers of charter schools includes strength of legislation, possibility of charter school management by for-profit companies, quality of traditional schools in the state, the number of years the law has been in effect, total Pre-K to 12 student population in the state, and proportion of minority students in the state. With the use of regression analysis, three of the variables were found to be significant and positively correlated to the total number of charter schools: total Pre-K to 12 student population in the state, strength of law, and number of years in effect. A reflection of how these results relate to the theoretical underpinnings of the study is included as well as suggestions for future research.Item Communication-based performance analysis of parallel processing machines(Texas Tech University, 2003-08) Parikh, SachinNot availableItem Computional aspects in time-series modeling(Texas Tech University, 1998-08) Phelps, David ANot availableItem Developmental responses of cotton genotypes to varying water application regimes(Texas Tech University, 2004-05) West-Emerson, Cora LeaNot availableItem Distribution-free interval estimation techniques for slope in simple linear regression(Texas Tech University, 1985-08) Taylor, Gregory ClarkThis work is concerned with estimating the slope parameter, B, in the simple linear regression model. Many of the rank procedures used to find confidence intervals for fl are free of distributional assumptions on the dependent variable. Rank procedures often generate smaller intervals than those given by the classical procedures when the residual distribution is skewed or heavy-tailed. Many of these procedures, such as those discussed by Theil, Sen, and Sievers, are computationally infeasible, even for a computer, when used with samples much larger than 100. This dissertation develops distribution-free procedures which substantially reduce these calculations. The rank-based procedures of Theil, Sen, and Sievers require n(n-1)/2 pairwise slopes to be computed. The new procedures require only n such pairwise slopes to be computed, a comparatively negligible amount of computation. Measures of asymptotic relative efficiency (ARE) and finite sample efficiency are derived and used to evaluate these "reduced" procedures. In many cases, the reduced procedures have efficiencies of .8 when compared to the procedures described by Theil, Sen, and Sievers. This is a small loss considering the computational gains from using the reduced procedures developed in this dissertation.Item Effects of particle-size distribution, organic matter and calcium carbonate on water retention of Texas High Plains soils(Texas Tech University, 1985-05) Meng, Tan PiowThe overall objective of the research program was to provide data that will allow the prediction of the probable effectiveness of precipitation when deep plowing has altered the texture of the plow layer.Item Efficient allocation of resources in human organ markets: is it achievable through economic tools?(Texas Tech University, 2004-05) Altinanahtar, AlperNot availableItem Empirical modeling of ballistic spall behavior(Texas Tech University, 1980-08) Lin, Wen-jungNot availableItem Evaluation of the conduit sizing portion of the design procedure for storm drain networks in Texas(Texas Tech University, 2004-08) Keister, Heather RaeNot availableItem Farm size, structure and irrigation practices: an exploratory study(Texas Tech University, 1981-08) Garibay, RayNot availableItem Fundamental structural changes over time and predictability of exchange rates: a Monte Carlo study of time varying regression and applications(Texas Tech University, 1996-08) Wan, BinThe difficulties of modeling and forecasting foreign exchange rates have been well known since early 1970's. One of the possible explanations for our inability to provide an accurate model is the structural changes over time, especially in emerging markets. The traditional regression techniques that assume constant parameters are incapable of capturing the changing dynamics over time. Consequently, most foreign exchange regression models are ineffective. To better capture fundamental structural changes in a market, a moving block regression technique is recommended by the author. The moving block regression procedure utilizes sub-sample information, rather than the prevailing whole sample data that intends to increase regression efficiency with more observations. To find out the loss or gain of forecast efficiency, a Monte Carlo study is carried out under several different scenarios: data in compliance with the classic OLS assumptions, data with heteroscedasticity, data with autocorrelation, model with a missing variable, model with changing regression coefficients, and data with nonlinear relationships. Simulation results show a trivial loss of out-of-sample forecast efficiency with the moving block regressions and a small trade-off in the presence of minor violations of the assumptions. However, there is a clear dominance of the moving block regressions over the traditional whole sample regressions in terms of forecasting efficiency when the violations of assumptions are serious, such as missing variable, changing coefficients, or nonlinear relations. Then the moving block regressions are applied to exchange rates of six currencies against the U.S. dollar. The comparisons of forecasting residuals, both in-sample and out-of-sample, show a strong support for the moving block techniques, indicating the inevitable violations of regression assumptions in foreign exchange markets.Item Left frontal lobe dysfunction in "Schizophrenic-like" college students(Texas Tech University, 1991-12) Weinstein-Richardson, LisaNot availableItem The life insurer Risk-Based Capital ratio : panel data analysis(2013-05) Beisenov, Aidyn; Sager, Thomas W.Many studies suggest the ability of the NAIC Risk-Based Capital ratio (RBC ratio) to predict insurer insolvency. Based on the US life insurer (insurer) data for the period of 2005 to 2008, this study finds explanatory variables that have a statistically significant relationship with the RBC ratio. Advantages of panel data over cross-sectional and time series data analysis are exploited to make valid inference on coefficients of the explanatory variables. Testing for unobserved insurer and time effects and for dependence between these effects and the explanatory variables indicates the appropriateness of the fixed insurer and time effects model. Based on the ordinary least squares estimates, it is found that insurers' size, capital-to-asset ratio, and return on capital have a statistically significant relationship with the RBC ratio. Additionally, health product, annuity product, opportunity, and regulatory risks of insurers are related to the RBC ratio. Accounting for heteroscedasticity and autocorrelation for a given insurer yields the same coefficient estimates, but increased standard errors.Item Linguistic differences in personal and social communication contexts in dramatic literature(Texas Tech University, 1977-08) Jones, Nancy JuneNot availableItem Logistic regression applications and cluster analysis(Texas Tech University, 1998-12) Peterson, Jennifer KristiThe objectives of this thesis are (1) to give a brief overview of the logistic regression model, (2) review some applications of the model. (3) present a comparison of the logistic regression model with the standard multiple regression model via examples, and (4) consider the use of cluster analysis as an aid in determining the groupings for a logistic regression analysis. This paper contains an overview of logistic regression and a brief discussion of cluster analysis along with applications of each. Chapter II covers the basic preliminary ideas surrounding logistic regression including how it differs from linear regression. Different estimation techniques are also discussed. Chapter III considers various areas of logistic regression through summaries of four concrete applications. Chapter IV specifically considers the analysis of two data sets by beginning with a preliminary analysis and exploring different model selection techniques to find the most appropriate model. These same concepts are reconsidered when interactions among the variables are also included.
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