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Showing 1 to 20 of 53 for “"nonparametric regression"”.
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Model Selection, Uniform Inference and Nonparametric Regression
Model selection in the nonparametric regression model is inevitable since any nonparametric estimator requires tuning parameters to be specified in order for it to be feasible. It is, however, standard practice to carry over the theory of nonparametric estimators when the model is fixed to the case …
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Symmetry and Other Structures: Topics in Nonparametric Regression
In regression we measure covariate samples X_i and response variables Y_i and seek to estimate the conditional mean, or regression function, f(x) = E(Y_i ∣ X_i = x) within some hypothesis class F of possible regression functions. Estimation of a nonparametric regression function f ∶ [0, 1]^d → R …
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Some topics on robust nonparametric regression and regression quantiles
… and B-spline estimates are considered in the nonparametric regression and the generalized linear models. The consistency and asymptotic normality of kernel estimates are proved. Simulations on B-spline estimates for nonparametric regression and generalized linear models are provided.
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Theory and applications of nonparametric regression in item response theory
The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. In particular, the joint asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric item characteristic curve estimation is investigated under relatively …
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Comparison of several curves in the context of nonparametric regression
Consider the model $y\sb{lj} = \mu\sb{l}(t\sb{j})$ + $\varepsilon\sb{lj}$, $l = 1,..,m$ and $j = 1,..,n,$ where $\varepsilon\sb{lj}$ are independent mean zero finite variance random variables. Under the above setting we test the hypotheses
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CROSS VALIDATION METHOD ON WEIGHTED ISOTONIC REGRESSION FOR NONPARAMETRIC REGRESSION FITTING
This thesis discusses the local polynomial regression and the isotonic regression method to solve the nonparametric regression problem subject to the non-decreasing condition. To solve the continuous isotonic regression problem, Pool Adjacent Violators Algorithm (PAVA) is used by updating the local …
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
… have become ubiquitous. We introduce blind regression, a framework motivated by matrix completion for recommender systems: given m users, n items, and a subset of user-item ratings, the goal is to predict the unobserved ratings given the data, i.e., to complete the partially observed matrix. …
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… this work, we provide an overview of different nonparametric methods for prediction interval estimation and investigate how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear …
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Bayesian Inference Based on Nonparametric Regression for Highly Correlated and High Dimensional Data
… data. Firstly, group multi-kernel machine regression (GMM) is proposed to identify the association between two sets of multidimensional functions, offering flexibility to effectively capture the complex association among high-dimensional variables. Secondly, semiparametric kernel machine …
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Canonical solution groups of a homomorphism : manipulator kinematics, nonparametric regression and distributed object systems
… to Cartesian space of a radial basis function regression correction of an affine stereo model. Basic design and performance requirements are defined for scalable virtual micro-kernels that broker inter-Java-virtual-machine remote method invocations between components of secure manageable …
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Divide and recombined for large complex data: Nonparametric-regression modelling of spatial and seasonal-temporal time series
… dissertation, I briefly introduce one type of nonparametric regression method, namely local polynomial regression, followed by emphasis on one specific application of loess on time series decomposition, called Seasonal Trend Loess (STL). The chapter is closed by the introduction of D\&R; …
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Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (<I>Pinus taeda</I> L.)
… L.) was described using single-regressor, nonparametric regression analysis in an effort to improve crown representations. The resulting profiles were compared to more traditional representations. Nonparametric regression may be applicable when an underlying parametric model cannot be …
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Nonparametric prediction in survey sampling and its application to the nonresponse problem
Nonparametric regression provides an important tool towards exploring the relationship between a dependent variable and the independent variable(s) without assuming a functional form between the variables. This thesis incorporates the nonparametric regression methodology in the context of …
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Parametric and Semi-Parametric Regression Estimation of the Effect of the United States Soybean Quality Attributes on Export Price
Results of both parametric and nonparametric regression estimations confirmed that some of the quality variables had a nonlinear relationship with export price. The study identified two possible sources of non-linearity of implicit price. The first was related to different degrees of competition in …
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Exploring Alternative Methodologies for Robust Inferences: Applications in Environmental and Health Economics
… economics. The first chapter proposes a novel nonparametric regression model to draw credible insights from meta-analyses. Existing literature on benefit-transfer validity is examined as an application. Nonparametric regression is found to be a viable approach for drawing robust policy …
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Recursive methods for statistical prediction with applications
Recursive methods for solving the nonparametric regression problem in the GLIMs and computing the Best Linear Unbiased Predictors are discussed here. An iterated state space algorithm is introduced to compute the generalized smoothing spline estimate, and it is especially useful in calculating the …
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Risk adjusted rate of return: Directional distance function approach
… This observation is confirmed through both nonparametric ranking test and nonparametric regression technique. Especially, the underlying return on equity (ROE) significantly influences the corresponding security performance.
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Shrinkage Estimation for Aalen's Additive Model
… (or in general event history). In particular, regression models that relate event occurrence rates to predictor variables are quite common in the medical field. One such regression model is the Aalen's nonparametric additive model in which the regression coefficients are assumed to be …
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The role of polls for election forecasting in German state elections
… to generate daily data to apply parametric regression based models. To forecast single vote shares in multi-party elections, the range of methods varies from basic methods like averaging over nonparametric regression based methods to dynamic linear models.
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