نتایج جستجو برای: time varying coefficient

تعداد نتایج: 2117486  

Journal: :Computational Statistics & Data Analysis 2008
Heung Wong Wai-Cheung Ip Riquan Zhang

In this paper, the varying-coefficient single-indexmodel (VCSIM) is proposed. It can be seen as a generalization of the semivaryingcoefficient model by changing its constant coefficient part to a nonparametric component, or a generalization of the partially linear single-indexmodel by replacing the constant coefficients of its linear part with varying coefficients. Based on the local linear met...

2009
Yichao Wu

NCSU, Princeton University, and UC-Davis Abstract: Functional linear regression analysis aims to model regression relations which include a functional predictor. The analogue to the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If in addition one has scalar predictors, ...

2012
Siliang Wang Minghui Wang

Many exist studies always use Markov decision processes (MDPs) in modeling optimal route choice in stochastic, time-varying networks. However, taking many variable traffic data and transforming them into optimal route decision is a computational challenge by employing MDPs in real transportation networks. In this paper we model finite horizon MDPs using directed hypergraphs. It is shown that th...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه صنعتی اصفهان - دانشکده ریاضی 1390

abstract: in the paper of black and scholes (1973) a closed form solution for the price of a european option is derived . as extension to the black and scholes model with constant volatility, option pricing model with time varying volatility have been suggested within the frame work of generalized autoregressive conditional heteroskedasticity (garch) . these processes can explain a number of em...

ژورنال: اندیشه آماری 2021

Varying coefficient Models are among the most important tools for discovering the dynamic patterns when a fixed pattern does not fit adequately well on the data, due to existing diverse temporal or local patterns. These models are natural extensions of classical parametric models that have achieved great popularity in data analysis with good interpretability.The high flexibility and interpretab...

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