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   Articles with "autoregressive models" as a keyword



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Asymptotics of self-weighted M-estimators for autoregressive models

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recommendations! Published in 2017 at "Metrika"

DOI: 10.1007/s00184-016-0592-x

Abstract: In this paper, we consider a stationary autoregressive AR(p) time series $$y_t=\phi _0+\phi _1y_{t-1}+\cdots +\phi _{p}y_{t-p}+u_t$$yt=ϕ0+ϕ1yt-1+⋯+ϕpyt-p+ut. A self-weighted M-estimator for the AR(p) model is proposed. The asymptotic normality of this estimator is established, which includes… read more here.

Keywords: weighted estimators; autoregressive models; self weighted; asymptotics self ... See more keywords
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A new approach to modeling temperature‐related mortality: Non‐linear autoregressive models with exogenous input

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recommendations! Published in 2018 at "Environmental Research"

DOI: 10.1016/j.envres.2018.02.020

Abstract: &NA; Temperature‐mortality relationships are nonlinear, time‐lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates… read more here.

Keywords: temperature; narx models; models exogenous; autoregressive models ... See more keywords
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Interaction matrix selection in spatial autoregressive models with an application to growth theory

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recommendations! Published in 2019 at "Regional Science and Urban Economics"

DOI: 10.1016/j.regsciurbeco.2019.01.002

Abstract: The interaction matrix, or spatial weight matrix, is the fundamental tool to model cross-sectional interdependence between observations in spatial autoregressive models. However, it is most of the time not derived from theory, as it should… read more here.

Keywords: interaction matrix; application; interaction; spatial autoregressive ... See more keywords
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Variable selection for spatial autoregressive models

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recommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2019.1649428

Abstract: Abstract This paper considers variable selection for spatial autoregressive models based on the minimum prediction error criterion. Firstly, based on an initial consistent estimator, a new loss function is constructed from the perspective of prediction,… read more here.

Keywords: selection spatial; autoregressive models; method; selection ... See more keywords
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Random autoregressive models: A structured overview

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recommendations! Published in 2020 at "Econometric Reviews"

DOI: 10.1080/07474938.2021.1899504

Abstract: Abstract Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sector-specific, overlapping, and… read more here.

Keywords: structured overview; autoregressive models; models structured; random autoregressive ... See more keywords
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About predictions in spatial autoregressive models: optimal and almost optimal strategies

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recommendations! Published in 2017 at "Spatial Economic Analysis"

DOI: 10.1080/17421772.2017.1300679

Abstract: ABSTRACT About predictions in spatial autoregressive models: optimal and almost optimal strategies. Spatial Economic Analysis. This paper addresses the problem of prediction in the spatial autoregressive (SAR) model for areal data, which is classically used… read more here.

Keywords: models optimal; optimal almost; almost optimal; autoregressive models ... See more keywords
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Integrated Identification of the Nonlinear Autoregressive Models With Exogenous Inputs (NARX) for Engineering Systems Design

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recommendations! Published in 2022 at "IEEE Transactions on Control Systems Technology"

DOI: 10.1109/tcst.2022.3171130

Abstract: This brief presents a new framework for the identification of nonlinear autoregressive (AR) models with exogenous inputs (NARX) model for design (NARX-M-for-D), which represents NARX of engineering systems where the model coefficients are represented explicitly… read more here.

Keywords: identification nonlinear; autoregressive models; design; model ... See more keywords
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Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

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recommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2021.3116668

Abstract: Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversity,… read more here.

Keywords: energy based; deep generative; autoregressive models; generative modelling ... See more keywords

Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks

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recommendations! Published in 2019 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2019.2940122

Abstract: Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in… read more here.

Keywords: time; structural vector; autoregressive models; vector autoregressive ... See more keywords
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Variable Selection of Heterogeneous Spatial Autoregressive Models via Double-Penalized Likelihood

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recommendations! Published in 2022 at "Symmetry"

DOI: 10.3390/sym14061200

Abstract: Heteroscedasticity is often encountered in spatial-data analysis, so a new class of heterogeneous spatial autoregressive models is introduced in this paper, where the variance parameters are allowed to depend on some explanatory variables. Here, we… read more here.

Keywords: via double; autoregressive models; double penalized; spatial autoregressive ... See more keywords

Asymptotic Properties of Quasi-Maximum Likelihood Estimators for Heterogeneous Spatial Autoregressive Models

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recommendations! Published in 2022 at "Symmetry"

DOI: 10.3390/sym14091894

Abstract: In this paper, we address a class of heterogeneous spatial autoregressive models with all n(n−1) spatial coefficients taking m distinct true values, where m is independent of the sample size n, and we establish asymptotic… read more here.

Keywords: quasi maximum; autoregressive models; spatial autoregressive; asymptotic properties ... See more keywords