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Dissertations / Theses on the topic 'Partially linear single index'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Zhang, Yuankun. "(Ultra-)High Dimensional Partially Linear Single Index Models for Quantile Regression." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535703962712806.

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2

Apanasovich, Tatiyana Vladimirovna. "Testing for spatial correlation and semiparametric spatial modeling of binary outcomes with application to aberrant crypt foci in colon carcinogenesis experiments." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2674.

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In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.
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3

Petry, Sebastian. "Regularization approaches for generalized linear models and single index models." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-143983.

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4

Petry, Sebastian [Verfasser]. "Regularization Approaches for Generalized Linear Models and Single Index Models / Sebastian Petry." München : Verlag Dr. Hut, 2012. http://d-nb.info/1023435241/34.

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5

Petry, Sebastian Verfasser], and Gerhard [Akademischer Betreuer] [Tutz. "Regularization approaches for generalized linear models and single index models / Sebastian Petry. Betreuer: Gerhard Tutz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2011. http://d-nb.info/1023435586/34.

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6

Chen, Jinsong. "Semiparametric Methods for the Generalized Linear Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28012.

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The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution. In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood approach is proposed to estimate the nonparametric function and parameters including single-index coe±cients in the GSSIMM. We estimate variance components using marginal quasi-likelihood. Asymptotic properties of the estimators are developed using a similar idea by Yu (2008). A simulation example is carried out to compare the performance of the GSSIMM with that of the GLMM. We demonstrate the advantage of my approach using a study of the association between daily air pollutants and daily mortality adjusted for temperature and wind speed in various counties of North Carolina.
Ph. D.
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7

Mahmoud, Hamdy Fayez Farahat. "Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/76744.

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Semiparametric modeling is a hybrid of the parametric and nonparametric modelings where some function forms are known and others are unknown. In this dissertation, we have made several contributions to semiparametric modeling based on the single index model related to the following three topics: the first is to propose a model for detecting change points simultaneously with estimating the unknown function; the second is to develop two models for spatially correlated data; and the third is to further develop two models for spatially-temporally correlated data. To address the first topic, we propose a unified approach in its ability to simultaneously estimate the nonlinear relationship and change points. We propose a single index change point model as our unified approach by adjusting for several other covariates. We nonparametrically estimate the unknown function using kernel smoothing and also provide a permutation based testing procedure to detect multiple change points. We show the asymptotic properties of the permutation testing based procedure. The advantage of our approach is demonstrated using the mortality data of Seoul, Korea from January, 2000 to December, 2007. On the second topic, we propose two semiparametric single index models for spatially correlated data. One additively separates the nonparametric function and spatially correlated random effects, while the other does not separate the nonparametric function and spatially correlated random effects. We estimate these two models using two algorithms based on Markov Chain Expectation Maximization algorithm. Our approaches are compared using simulations, suggesting that the semiparametric single index nonadditive model provides more accurate estimates of spatial correlation. The advantage of our approach is demonstrated using the mortality data of six cities, Korea from January, 2000 to December, 2007. The third topic involves proposing two semiparametric single index models for spatially and temporally correlated data. Our first model has the nonparametric function which can separate from spatially and temporally correlated random effects. We refer it to "semiparametric spatio-temporal separable single index model (SSTS-SIM)", while the second model does not separate the nonparametric function from spatially correlated random effects but separates the time random effects. We refer our second model to "semiparametric nonseparable single index model (SSTN-SIM)". Two algorithms based on Markov Chain Expectation Maximization algorithm are introduced to simultaneously estimate parameters, spatial effects, and times effects. The proposed models are then applied to the mortality data of six major cities in Korea. Our results suggest that SSTN-SIM is more flexible than SSTS-SIM because it can estimate various nonparametric functions while SSTS-SIM enforces the similar nonparametric curves. SSTN-SIM also provides better estimation and prediction.
Ph. D.
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8

Ohinata, Ren Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.

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9

Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.

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10

Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:7-webdoc-3831-5.

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11

Duprey, Benjamin Lawrence Blake. "A New Fuzzy Based Stability Index Using Predictive Vehicle Modeling and GPS Data." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/32762.

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The use of global positioning systems, or GPS, as a means of logistical organization for fleet vehicles has become more widespread in recent years. The system has the ability to track vehicle location, report on diagnostic trouble codes, and keep tabs on maintenance schedules. This helps to improve the safety and productivity of the vehicles and their operators. Additionally, the increasing use of yaw and roll stability control in commercial trucks has contributed to an increased level of safety for truck drivers. However, these systems require the vehicle to begin a yaw or roll event before they assist in maintaining control. This thesis presents a new method for utilizing the GPS signal in conjunction with a new fuzzy logic-based stability index, the Total Safety Margin (TSM), to create a superior active safety system.

This thesis consists of four main components:

  1. An overview of GPS technology is presented with coverage of several automotive-based applications. The proposed implementation of GPS in the new Hardware-in-the-Loop (HIL) driving simulator under development at the Virginia Tech Center for Vehicle Systems and Safety (CVeSS) is presented.
  2. The three degree-of-freedom (3DOF), linear, single track equation set used in the Matlab simulations is derived from first principles.
  3. Matlab and TruckSim 7® simulations are performed for five vehicle masses and three forward velocities in a ramp-steer maneuver. Using fuzzy logic to develop the control rules for the Total Safety Margin (TSM), TSM matrices are built for both the Matlab and TruckSim 7® results based on these testing conditions. By comparing these TSM matrices it is shown that the two simulation methods yield similar results.
  4. A discussion of the development and implementation of the aforementioned HIL driving simulator is presented, specifically the steering subsystem. Using Matlab/Simulink, dSPACE ControlDesk, and CarSim RT® software it is shown that the steering module is capable of steering the CarSim RT® simulation vehicle accurately within the physical range of the steering sensor used.

      5. Master of Science
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12

Zhang, Wei-lun, and 張瑋倫. "Variable Bandwidth Selection in Partially Linear Single-Index Model with incomplete data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/72349971428965806149.

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碩士
國立彰化師範大學
數學系所
96
In this thesis, we consider a heteroscedastic partially linear single index model (PLSIM) in which the response is censored and the regressor has measurement error. Inspired by Fan and Gijbels (1992) and Ye, Hyndman and Li (2006), we exploit a variable bandwidth to improve the estimation of the nonparametric part on the boundary in PLSIM. In their papers, an optimal variable bandwidth was proposed; however, it requires the partial derives of second order which are in general unknown. Adopting Li's (1992) ap- proach, assuming the underlying covariates to be normally distributed, we can calculate the Hessian matrix. Plugging the estimated Hessian matrix in to the expression of the variable bandwidth suggested by Ye, Hyndman and Li (2006), we found that variable bandwidth obtain better estimates than constant bandwidth. At the end of the thesis, we conduct some illustrative simulations to justify our method.
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13

Lin, Yin-Ying, and 林吟盈. "On Partially Linear Single-Index Models with Missing Response and Error-Inclined Predictors." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/40695975355010852643.

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碩士
國立彰化師範大學
統計資訊研究所
96
Survey data collection is a very efficient way to gather information for research of interest but often plagued with problems of incomplete data. In this thesis, we consider a partially linear single index model when the responses are missing at random and the nonlinear regressors are measured with error. Wang and Sun (2007) considered a partially linear model and adopted semiparametric imputation, semiparametric regression surrogate and inverse marginal probability weighted approaches, separately, to estimate the linear coefficients and the nonparametric function. Based on regression calibration proposed by Carroll and Li (1992) in dealing with the measurement error model, we are liable to deal with the estimation problem caused by measurement error. Fusing Wang and Sun's data imputation and Carroll and Li's calibration, we modify the first two approaches adopted by Wang and Sun (2007) to tackle missing responses and error-prone covariate in the nonparametric part. We not only estimate the parameters in the linear part, but also the single-index part in which the contaminated covariate is adherent. All the proposed estimators for the regression coefficients are shown to be asymptotically normal, and the illustrative simulation results are provided to justify our methods.
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14

Sewalem, Asheber. "Semiparametric Analysis of Survival Data with Applications in Agricultural Science." Thesis, 2012. http://hdl.handle.net/10214/3650.

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This thesis explores the association between a response variable and various regressors in dairy cattle breeding data using the various survival models in general and the partially linear single index survival model (PLSISM) in particular. In this study calf survival data and culling data were used. The calf survival data contains the following information: survival time, birth weight, weaning weight, calving ease score, average daily gain, number of disease incidences and serum total protein content. The culling data include, survival time, herd size variation, production level (milk, fat and protein), type of supervision, body condition score and age at first calving. Both data sets contain herd, year and season of calving and were analyzed using the various survival models. The Weibull model, however, was used for detailed analyses of the data sets. The nonparametric vector of PLSISM includes body weight, total serum protein and average daily gain for calf survival data and age at first calving, fat production and body condition core for culling data. The parametric vector of PLSISM consists of the rest of the covariates. The results show that the estimates of the parametric component are similar in the two models (Weibull and PLSISM). However, the estimates of the nonparametric component differ from parametric analysis. This difference may be attributed largely to the nonlinearity of the estimated function indicating the standard linear survival model does not adequately describe the underlying association between the response variable and the various covariates in this study. This is the first implementation and application of this complex model, PLSISM, with large real censored data.
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15

Ohinata, Ren. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model." Doctoral thesis, 2012. http://hdl.handle.net/11858/00-1735-0000-000D-F0A2-0.

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16

Gurses, Kerem. "Dynamic modeling and vibration control of a single-link flexible manipulator using a combined linear and angular velocity feedback controller." Thesis, 2007. http://hdl.handle.net/1828/1237.

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The use of lightweight, thin flexible structures creates a dilemma in the aerospace and robotic industries. While increased operating efficiency and mobility can be achieved by employing such structures, these benefits are compromised by significant structural vibrations due to the increased flexibility. To address this problem, extensive research in the area of vibration control of flexible structures has been performed over the last two decades. The majority of the research has been based on the use of discrete piezoceramic actuators (PZTs) as active dampers, as they are commercial availability and have high force and bandwidth capabilities. Many different active vibration control strategies have previously been proposed, in order to effectively suppress vibrations. The synthesized vibration controllers will be less effective or even make the system to become unstable if the actuator locations and control gains are not chosen properly. However, there is currently no quantitative procedure that deals with these procedures simultaneously. This thesis presents a theoretical and numerical study of vibration control of a singlelink flexible manipulator attached to a rotating hub, with PZTs bonded to the surface of the link. A commercially available fibre optic sensor called ShapeTapeTM is introduced as a new feedback sensing technique, which is complemented by a quantitative and definitive model based procedure for selecting the individual PZT locations and gains. Based on Euler-Bernoulli beam theory, discrete finite element equations are obtained using Lagrange’s equations for a PZT-mounted beam element. Slewing of the flexible link by a rotating hub induces vibrations in the link that persist long after the hub stops rotating. These vibrations are suppressed through a combined scheme of PD-based hub motion control and proposed PZT actuator control, which is a composite linear (L-type) and angular (A-type) velocity feedback controller. A Lyapunov approach was used to synthesize the PZT controller. The feedback sensing of linear and angular velocities is realized by using the ShapeTapeTM, which measures the bend and twist of the flexible link’s centerline. Both simulation and experimental results show that tip vibrations are most effectively suppressed using the proposed composite controller. Its performance advantage over the individual linear or angular velocity feedback controllers confirms theoretical predictions made based on a non-proportional damping model of the PZT effects. Furthermore, it is demonstrated that the non-proportional nature of the PZT damping effect must be considered in order to bound the range of allowable controller gain values.
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