Browsing by Subject "Analysis of variance"
Now showing 1 - 20 of 23
Results Per Page
Sort Options
Item A comparison of coping skills of chronic, non-malignant pain patients and cancer patients with chronic pain(Texas Tech University, 2002-05) Tice, ToddWhile the effectiveness of various coping strategies in chronic pain patients has been well-documented, a similar research focus has not been applied to chronic pain patients diagnosed with cancer. Religious activities used for coping with pain have also rarely been investigated in either population. This study was designed to determine if differences between the coping skills of chronic pain patients without cancer and those with cancer exist. The effect of coping skills, including religious activities, upon the perception of pain was examined via self-report instruments. The Coping Styles Questionnaire was used to examine differences in secular coping styles, and Religious Coping Activities Scale was used to examine differences in religion oriented coping activities. A numerical rating scale and the Pain Discomfort scale were used to assess differences in pain levels and affect. Analysis of the results suggests no differences in secular coping styles or pain levels existed between the two samples, but some differences in religious coping activities were found. Item in scales representing Spirituality, Good Deeds, Support, and Avoidance were more strongly endorsed by chronic pain patients with cancer.Item A distribution-free generalization of the Wilcoxon signed-ranks test for the two-way layout(Texas Tech University, 1978-12) Lawler, Kenneth RNot availableItem Analysis of covariance: the treatment of subjects as groups in an illustrative application with a baseball model(Texas Tech University, 1979-12) Williams, Larry RobertKim and Kohout (19 75) have noted that most texts devoted to a discussion of analysis of variance and/or covariance routinely assume that the collected data are experimental in nature and employ random assignment. However, social and behavioral scientists are increasingly dealing with variables which are nonmanipulative and designs which are observational rather than experimental. Wildt and Ahtola (1978) have proposed that analysis of covariance has numerous potential applications for behavioral research even though it has not been frequently utilized for problems in the various social science disciplines. Therefore, the major motivation behind this study was directed toward broadening the social scientist's currently restricted range of utilized methodologies and encouraging opportunistic and creative exploitation of unique measurement possibilities, especially with regard to the statistical technique of analysis of covariance.Item Approximations to the exact distribution of the Kruskal-Wallis test statistic for unequal sample sizes(Texas Tech University, 1976-12) Wynn, Terry DuaneNot availableItem Automated variance reduction for Monte Carlo shielding analyses with MCNP(2003) Radulescu, Georgeta; Landsberger, Sheldon; Tang, Jabo S.Item Data-driven approach for control performance monitoring and fault diagnosis(2007) Yu, Jie; Qin, S. JoeDue to the industrial value, control performance and process monitoring have attracted increasing attention in recent years. However, there still exist challenges that restrict the industrial applications of monitoring technology. This dissertation presents some innovative solutions to the monitoring issues. To avoid the interactor requirement of minimum variance control (MVC) benchmark, a data-driven covariance monitoring framework is established. Relative to a user-defined benchmark, generalized eigenvalue analysis is employed to extract the directions with the worst performance. A statistical inference strategy is then developed to identify the worse or better performance directions and subspace. The covariance based indices are further derived to assess the performance degradation or improvement. To diagnose the controlled variables causing the performance change, two types of multivariate contribution methods are proposed. One is to evaluate the significance of the eigenvector loadings while the other to examine the angle between each variable and the worse/better subspace. Complementary to the data-driven performance monitoring scheme, a simplified solution to MVC benchmark is also developed. A right diagonal interactor is first factorized from process time delays and the corresponding MVC benchmark is derived with numerical simplicity. For more general MIMO processes, left and right diagonal interactors are integrated to characterize the more complex delay structure. The MVC estimation using the left/right diagonal interactors are presented. To further improve multivariable control performance, an iterative strategy of output weighting selection is proposed. Eigenvalue decomposition is implemented on the output covariance to find the directions with the largest variance inflation. A nondiagonal weighting matrix is then designed with respect to the eigendirections and more importance proportional to the corresponding eigenvalues is assigned. In addition to control performance monitoring, process monitoring is also investigated with focus on fault detection and diagnosis of multistage overlay lithography processes. In our work, a multistage state-space model for the misalignment errors is developed from the physical principles and then formulated into the general mixed linear model. Subsequently, variance component analysis is employed to estimate the mean and variance components of the potential fault sources. A hypothesis testing procedure is adopted to detect the active faults in different layers while the mean/variance estimates are used to diagnose their magnitude and orientation.Item Data-driven approach for control performance monitoring and fault diagnosis(2007-05) Yu, Jie, 1977-; Qin, S. JoeDue to the industrial value, control performance and process monitoring have attracted increasing attention in recent years. However, there still exist challenges that restrict the industrial applications of monitoring technology. This dissertation presents some innovative solutions to the monitoring issues. To avoid the interactor requirement of minimum variance control (MVC) benchmark, a data-driven covariance monitoring framework is established. Relative to a user-defined benchmark, generalized eigenvalue analysis is employed to extract the directions with the worst performance. A statistical inference strategy is then developed to identify the worse or better performance directions and subspace. The covariance based indices are further derived to assess the performance degradation or improvement. To diagnose the controlled variables causing the performance change, two types of multivariate contribution methods are proposed. One is to evaluate the significance of the eigenvector loadings while the other to examine the angle between each variable and the worse/better subspace. Complementary to the data-driven performance monitoring scheme, a simplified solution to MVC benchmark is also developed. A right diagonal interactor is first factorized from process time delays and the corresponding MVC benchmark is derived with numerical simplicity. For more general MIMO processes, left and right diagonal interactors are integrated to characterize the more complex delay structure. The MVC estimation using the left/right diagonal interactors are presented. To further improve multivariable control performance, an iterative strategy of output weighting selection is proposed. Eigenvalue decomposition is implemented on the output covariance to find the directions with the largest variance inflation. A nondiagonal weighting matrix is then designed with respect to the eigendirections and more importance proportional to the corresponding eigenvalues is assigned. In addition to control performance monitoring, process monitoring is also investigated with focus on fault detection and diagnosis of multistage overlay lithography processes. In our work, a multistage state-space model for the misalignment errors is developed from the physical principles and then formulated into the general mixed linear model. Subsequently, variance component analysis is employed to estimate the mean and variance components of the potential fault sources. A hypothesis testing procedure is adopted to detect the active faults in different layers while the mean/variance estimates are used to diagnose their magnitude and orientation.Item Distribution-free procedures for testing and pairwise comparisons under order restrictions in incomplete blocks(Texas Tech University, 1998-08) Shaw, Carrie Nicole SmartNot availableItem Effects of mental imagery training upon self-attitude(Texas Tech University, 1979-05) Patrizi, Fredric MarkNot availableItem The impact of the inappropriate modeling of cross-classified data structures(2004) Meyers, Jason Leon; Beretvas, Susan NatashaItem Lagrangian multiplier in the Pearlmutter algorithm and dynamic neural networks(Texas Tech University, 1998-05) Sun, JuanNot availableItem Nonparametric methods for pairwise comparisons in the randomized complete block design(Texas Tech University, 1998-12) Barefield, Eric W.The main focus of this investigation is to verify the robustness of validity of these tests. By robustness of validity, it is meant the stability of the Type I error rate for small designs as well as conditions under which some of the underlying assumptions are violated. For methods that produce valid tests, further investigations with respect to power are meaningful. Since many nonparametric methods use large sample approximation theory, it may be expected that these methods will perform better as n, the number of blocks, increases. As the number of treatments, p, gets larger, it becomes harder to reject the null hypothesis since this increases the critical value. As we will see, some design structures make rejection of the null hypothesis impossible for certain methods. The methods of interest include the sign statistic, signed rank statistic, rank sum statistic (Aligned Rank statistic using separated rankings and uniform scores). Aligned-Rank Transform, Within-Block ranking, and least squares.Item Power of nonparametric tests for location/scale in one-factor model(Texas Tech University, 1998-08) Smart, Sandi ReneeNot availableItem Regression analysis with correlated observations(Texas Tech University, 1983-08) Scariano, Stephen MarkNot availableItem Statistical criteria for using certain Stein-type empirical Bayes estimators(Texas Tech University, 1979-05) Kakar, Rajesh KumarNot availableItem The effectiveness of EMG biofeedback training compared to ritalin (methylphenidate) in the management of hyperkinesis(Texas Tech University, 1978-12) Childress, Robert NeyNot availableItem The effects of cotton blending on warehousing cost(Texas Tech University, 1980-12) Collings, Melvin DouglasNot availableItem The effects of intraclass correlation on factorial experiments(Texas Tech University, 1980-08) Smith, James H.Not availableItem Toward a comprehensive hazard-based duration framework to accomodate nonresponse in panel surveys(2002) Zhao, Huimin; Bhat, Chandra R. (Chandrasekhar R.), 1964-Many surveys suffer from low response rates and therefore carry a risk of nonresponse bias. The problem is more severe in panel surveys because sample units are subject to nonresponse repeatedly. This dissertation is concerned with nonresponse in longitudinal household travel surveys. It identifies the likely sources of nonresponse and investigates a model-based bias correction procedure for the subject of interest in the survey -- trip frequency. Low response rates often lead to a sample representativeness problem and threaten the validity of the survey. A better understanding of the survey participation behavior can provide guidance for survey design to increase the response rates and to build an effective nonresponse bias correction procedure. It is generally believed that nonresponse is a combined result of social environment, survey attributes, and characteristics of sample units. In addition, state dependence and the lagged impact of exogenous variables can not be ignored when considering repeated responses in panel surveys. The first stage of this work considers the repeated participation in panel surveys as a duration process and proposes a hazard-based duration model for the analysis. The model structure accommodates state dependence and the lagged effects in a straightforward manner. Various factors, especially the indicators of survey burden, are incorporated in the model for a comprehensive understanding of the survey participation decision. The empirical analysis based on the seven-wave Puget Sound Transportation Panel suggests that survey burden, in general, is negatively associated with the survey participation duration. The results also reflect an interactive impact of survey burden and time constraints on the survey participation. The second stage of this work further investigates the relationship between the survey participation and trip frequency. The model formulation incorporates observed and unobserved heterogeneity in the participation and travel decisions. It is found that trip frequency, especially for home-based non- work trips, is endogenously correlated with the survey participation decision and the ignorance of this endogenous correlation leads to a biased estimate for the trip frequency and survey participation duration.Item Two sample comparisons with mixed discrete and continuous variants(Texas Tech University, 1996-08) Xu, WenThe comparisons of means, dispersions or distributions are well understood and researched for univariate investigations. Much literature is available for the design and implementafion of studies involving muhivariate normal samples. Most of investigations into muhivariate, non-normal comparisons still remain open. This dissertation presents what is available and provides two simple methods to transform the muhivariate two sample comparison into the well known chi-square test or logistic model. The methodologies and techniques will be examined by simulations and utilized to analyze the data from Texas Tech Health Science Center on the screening of Alzheimer's disease.