The phrase business cycle is usually used for short term fluctuations in macroeconomic time series. In this paper we focus on the estimation of business cycles in a bivariate manner by fitting two series simultaneously. The underlying model is thereby nonparametric in that no functional form is prespecified but smoothness of the functions are assumed. The functions are then estimated using pena...

We study the Cox models with semiparametric relative risk, which can be partially linear with one nonparametric component, or multiple additive or nonadditive nonparametric components. A penalized partial likelihood procedure is proposed to simultaneously estimate the parameters and select variables for both the parametric and the nonparametric parts. Two penalties are applied sequentially. The...

In this paper, we describe research in fundamental frequency modeling based on a statistical learning technique called additive models. A two-layer additive F0 model consists of a long-term, intonational phrase-level component, and a short-term, accentual phrase-level component. It can be learned from the data using a backfitting algorithm, an optimizer of a penalized least-square criterion def...

The performance of nine different nonparametric regression estimates is empirically compared on ten different real data sets. The number of data points in the real data sets varies between 7900 and 18000, where each real data set contains between 5 and 20 variables. The nonparametric regression estimates include kernel, partitioning, nearest neighbor, additive spline, neural network, penalized ...

Understanding the seizure initiation process and its propagation pattern(s) is a critical task in epilepsy research. Characteristics of the pre-seizure electroencephalograms (EEGs) such as oscillating powers and high-frequency activities are believed to be indicative of the seizure onset and spread patterns. In this article, we analyze epileptic EEG time series using nonparametric spectral esti...

We use a discrete-time proportional hazards model of time to involuntary employment termination. This model enables us to examine both the continuous effect of the age of an employee and whether that effect has varied over time, generalizing earlier work [Kadane and Woodworth J. Bus. Econom. Statist. 22 (2004) 182–193]. We model the log hazard surface (over age and time) as a thin-plate spline,...

Longitudinal data are routinely collected in biomedical research studies. A natural model describing longitudinal data decomposes an individual's outcome as the sum of a population mean function and random subject-specific deviations. When parametric assumptions are too restrictive, methods modeling the population mean function and the random subject-specific functions nonparametrically are in ...

This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained ...

[To be revised.] Quantile and expectile regression are tail oriented conditional regression. They can be transformed as generalized quantile regression. Traditional generalized quantile regression focuses on a single curve. When more random curves are available, we can estimate the single curves jointly by using the information from all subjects instead of estimate it individually. To avoid too...

Thomas Nagler*, Christian Schellhase, and Claudia Czado Nonparametric estimation of simpli ed vine copula models: comparison of methods DOI 10.1515/demo-2017-0007 Received December 27, 2016; accepted May 16, 2017 Abstract: In the last decade, simpli ed vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densiti...