Semiparametric Regression Research Papers - Academia.edu (original) (raw)

A novel semiparametric regression model for censored data is proposed as an alternative to the widely used proportional hazards survival model. The proposed regression model for censored data turns out to be flexible and practically... more

A novel semiparametric regression model for censored data is proposed as an alternative to the widely used proportional hazards survival model. The proposed regression model for censored data turns out to be flexible and practically meaningful. Features include physical interpretation of the regression coefficients through the mean response time instead of the hazard functions, and a rigorous proof of consistency of the posterior distribution. It is shown that the regression model obtained by a mixture of parametric families, has a proportional mean structure (as in an accelerated failure time models). The statistical inference is based on a nonparametric Bayesian approach that uses a Dirichlet process prior for the mixing distribution. Consistency of the posterior distribution of the regression parameters in the Euclidean metric is established. Finite sample parameter estimates along with associated measure of uncertainties can be computed by a MCMC method. Simulation studies are p...

In this paper we consider the semiparametric regression model, y=Xβ+f+εy=Xβ+f+ε. Recently, Hu [11] proposed ridge regression estimator in a semiparametric regression model. We introduce a Liu-type (combined ridge-Stein) estimator (LTE) in... more

In this paper we consider the semiparametric regression model, y=Xβ+f+εy=Xβ+f+ε. Recently, Hu [11] proposed ridge regression estimator in a semiparametric regression model. We introduce a Liu-type (combined ridge-Stein) estimator (LTE) in a semiparametric regression model. Firstly, Liu-type estimators of both ββ and ff are attained without a restrained design matrix. Secondly, the LTE estimator of ββ is compared with the two-step estimator in terms of the mean square error. We describe the almost unbiased Liu-type estimator in semiparametric regression models. The almost unbiased Liu-type estimator is compared with the Liu-type estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the estimators.

We estimate semiparametric regression models of chronic undernutrition (stunting) using the 1992 Demographic and Health Surveys (DHS) from Tanzania and Zambia. We focus particularly on the influence of the child's age, the... more

We estimate semiparametric regression models of chronic undernutrition (stunting) using the 1992 Demographic and Health Surveys (DHS) from Tanzania and Zambia. We focus particularly on the influence of the child's age, the mother's body mass index, and spatial influences on chronic undernutrition. Conventional parametric regression models are not flexible enough to cope with possibly nonlinear effects of the continuous covariates

This paper introduces a semiparametric regression estimator of the memory parameter for long-memory time series process. It is based on the regression in a neighborhood of the zero-frequency of the periodogram averaged over epochs. The... more

This paper introduces a semiparametric regression estimator of the memory parameter for long-memory time series process. It is based on the regression in a neighborhood of the zero-frequency of the periodogram averaged over epochs. The proposed estimator is theoretically justified and empirical Monte Carlo investigation gives evidence that the method is very promising to estimate the long-memory parameter.

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.... more

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters is greater than the number of observations. We derive an empirical Bayesian approach that allows us to estimate the prior smoothing hyperparameters from the data. An advantage of our semiparametric model is that it is written as a seemingly unrelated regressions model with independent Normal-Wishart prior. Since this model is a common one, textbook results for posterior inference, model comparison, prediction and posterior computation are immediately available. We use this model in an application involving a two-equation ...

Objective This ecologic study tested the hypothesis that census tracts with elevated groundwater uranium and more frequent groundwater use have increased cancer incidence. Methods Data sources included: incident total, leukemia, prostate,... more

Objective This ecologic study tested the hypothesis that census tracts with elevated groundwater uranium and more frequent groundwater use have increased cancer incidence. Methods Data sources included: incident total, leukemia, prostate, breast, colorectal, lung, kidney, and bladder cancers (1996–2005, SC Central Cancer Registry); demographic and groundwater use (1990 US Census); and groundwater uranium concentrations (n = 4,600, from existing federal and state databases). Kriging was used to predict average uranium concentrations within tracts. The relationship between uranium and standardized cancer incidence ratios was modeled among tracts with substantial groundwater use via linear or semiparametric regression, with and without stratification by the proportion of African Americans in each area. Results A total of 134,685 cancer cases were evaluated. Tracts with ≥50% groundwater use and uranium concentrations in the upper quartile had increased risks for colorectal, breast, kidney, prostate, and total cancer compared to referent tracts. Some of these relationships were more likely to be observed among tracts populated primarily by African Americans. Conclusion SC regions with elevated groundwater uranium and more groundwater use may have an increased incidence of certain cancers, although additional research is needed since the design precluded adjustment for race or other predictive factors at the individual level.

We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size... more

We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the special case of linear models, the procedure reduces to iterative reweighted least squares. Finite sample performance of the procedure is studied by simulations, and compared with other methods. A numerical example from a medical study is considered to illustrate the application of the method.

This study utilized a semiparametric panel model to estimate environmental Kuznets curves (EKC) for carbon dioxide (CO2) in 15 Latin American countries, using hitherto unused data on forestry acreage in each country. Results showed an... more

This study utilized a semiparametric panel model to estimate environmental Kuznets curves (EKC) for carbon dioxide (CO2) in 15 Latin American countries, using hitherto unused data on forestry acreage in each country. Results showed an N-shaped curve for the region; however, the shape of the curve is sensitive to the removal of some groups of countries. Specification tests support a semiparametric panel model over a parametric quadratic specification.

We prove -consistency and asymptotic normality of a generalized semiparametric regression estimator that includes as special cases Ichimura's semiparametric least-squares estimator for single index models, and the estimator of Klein... more

We prove -consistency and asymptotic normality of a generalized semiparametric regression estimator that includes as special cases Ichimura's semiparametric least-squares estimator for single index models, and the estimator of Klein and Spady for the binary choice regression model. Two function expansions reveal a type of U-process structure in the criterion function; then new U-process maximal inequalities are applied to establish the requisite stochastic equicontinuity condition. This method of proof avoids much of the technical detail required by more traditional methods of analysis. The general framework suggests other -consistent and asymptotically normal estimators.

Science is learning more about the brain activity necessary for consciousness, but has not identified any mechanisms for how it could actually arise through neural processes. Here I present ways to build consciousness into the mathematics... more

Science is learning more about the brain activity necessary for consciousness, but has not identified any mechanisms for how it could actually arise through neural processes. Here I present ways to build consciousness into the mathematics of quantum mechanics for use in the growing area of quantum neurology. Quantum waves are not physical in the sense of forces acting on particles through cause and effect, and they are expressed using mathematical placeholders that do not have agreed-upon real-world representations. These can be used to represent consciousness. Quantum mechanics has shown how the traditional aspects of the physical world emerge from non-physical quantum information through a combination of mathematical, not causal, determinism, and stochastic interactions. The modeling methods here could help account for how the mental world could also emerge from information fields. Neural interactions for mental experiences are complex, so it is reasonable to expect that consciousness is an emergent property of neural networks. But emergent properties are not magic-they work through procedural mechanisms-in this case for how neural processes generate experiences. No steps for how consciousness could be manufactured in this way are apparent, and philosophers have strong arguments for such not being possible. An alternative is to model consciousness as part of quantum waves, so it is accessed, not created, by the brain. This is a form of neutral monism-the idea that the physical and mental worlds both come from a single underlying source-in this case quantum waves. The classical physical picture of particles and forces acting under cause and effect has developed into a belief system, not just a theory, and this has created difficulties in taking quantum mechanics itself at face value. The result has been the creation of numerous "interpretations" of quantum mechanics that seek to frame it within these philosophical presuppositions. The conceptual framework of Cartesian dualism provides a platform for analysis of the related philosophical issues of consciousness and of quantum mechanics itself.

This paper analyses whether and to what extent the in flow of FDI is affected before and after the occurrence of a financial crisis in developing countries. The paper uses a semiparametric Generalized Partial Linear Models (GPLM)... more

This paper analyses whether and to what extent the in flow of FDI is affected before and after the occurrence of a financial crisis in developing countries. The paper uses a semiparametric Generalized Partial Linear Models (GPLM) regression approach to check the appropriateness and effectiveness of financial crisis in the FDI regression model. The results indicate that FDI in flows decrease in the years after a financial crisis and an upturn in FDI in flows the year before a financial crisis hit the country.

Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that... more

Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting