Results 121 to 130 of about 30,567 (245)

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

Journal of Time Series Analysis, EarlyView.
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley   +1 more source

Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data

Mathematics
In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses.
Shuanghua Luo, Yuxin Yan, Cheng-yi Zhang
doaj   +1 more source

Pointwise adaptive estimation for robust and quantile regression

, 2009
A nonparametric procedure for robust regression estimation and for quantile regression is proposed which is completely data-driven and adapts locally to the regularity of the regression function. This is achieved by considering in each point M-estimators
Cuenod, Charles-Andre, Reiss, Markus, Rozenholc, Yves   +2 more
core   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

Parameter Estimation of the Partially Linear Quantile Regression Model Under Monotonic Constraints

Journal of Mathematics
The paper brings forward the partially linear quantile regression model by incorporating monotonic constraints, which are common in real-world relationships between variables.
Shujin Wu, Zhilin Yu, Shanshan Liang, Yanke Ren   +3 more
doaj   +1 more source

Nonparametric instrumental variables estimation of a quantile regression model [PDF]

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.
Joel Horowitz, Sokbae 'Simon' Lee
core  

Inspecting the poverty-trap mechanism: a quantile regression approach [PDF]

, 2007
The issue of poverty traps is assessed using quantile regression. For that an augmentation of the usual convergence regressions by quadratic and cubic terms is used with emphasis on curve fitting rather than parameter estimation.
Krüger, Jens J.
core  

Analyzing Non‐Random Selectivity in Online Job Advertisements Using Eurostat Benchmark Data and Generalized Sample Selection Models: An Application to EU Regional Labor Markets

LABOUR, EarlyView.
ABSTRACT The present paper provides an overall framework to afford the problem of non‐representativeness and non‐random selectivity arising from online job ads data, using Generalized sample selection models and Eurostat benchmark data. We jointly model the outcome intensity (number of online job ads in observed profiles, whose levels are defined by ...
Pietro Giorgio Lovaglio, Mario Mezzanzanica   +1 more
wiley   +1 more source

An Algorithm of Nonparametric Quantile Regression [PDF]

, 2023
Mei Ling Huang, Yansan Han, William Marshall   +2 more
openalex   +1 more source

Estimating Correlations Between Clinical Trial Outcomes Using Generalised Estimating Equations

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT Accurately estimating the correlations among clinical trial outcomes is crucial for managing the risk of biopharmaceutical investment portfolios. We propose a novel algorithm for estimating correlations in large clinical trial datasets using a generalised estimating equations (GEE) framework.
Yuehao Dai, Andrew W. Lo, Manish Singh, Qingyang Xu, Ruixun Zhang   +4 more
wiley   +1 more source