Robust shrinkage estimation and selection for functional multiple linear model through LAD loss

Huang, Lele; Zhao, Junwei; Wang, Huiwen; Wang, Siyang

doi:10.1016/j.csda.2016.05.017

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…, m np , and the regularization parameter λ. Several criteria, such as generalized cross-validation (GCV, Lian, 2013), Schwarz information criterion (SIC, Huang et al, 2016) and ABIC procedure proposed by Kong et al (2016), can be used to select these tuning parameters simultaneously.…”

Section: Tuning Parameters Selectionmentioning

confidence: 99%

“…For example, Lian (2013) studied the variable selection problem for multiple functional linear regression via a group SCAD penalty; Kong et al (2016) incorporated scalar predictors into functional linear regression and proposed a shrinking estimation and selection procedure for partially functional linear regression in high dimensions; Yao et al (2017) introduced a regularized method for partially functional quantile regression model; Lin et al (2017) proposed a functional SCAD regularization procedure for functional linear regression models. Other variable selection study for functional regression can be found in the sequence of monographs by Zhou et al (2013), Huang et al (2016) and Ma et al (2019). Sang et al (2020) estimated a sparse functional additive model with the adaptive group LASSO approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Variable Selection for Multiple Function-on-Function Linear Regression

Cai¹,

Xue²,

Cao³

2022

STAT SINICA

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Section: Tuning Parameters Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable Selection for Multiple Function-on-Function Linear Regression

Cai¹,

Xue²,

Cao³

2022

STAT SINICA

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“…Functional linear model is among the most popular methods that have been widely used in the FDA (Ramsay and Silverman, 2007;Horváth and Kokoszka, 2012;Cai et al, 2006). Many results have been published on the functional linear model, in which only functional predictor is presented (Hall and Hooker, 2016;Comte et al, 2012;García-Portugués et al, 2014;Escabias et al, 2004;Shang et al, 2015;Huang et al, 2016). Nevertheless, these regression models are constrained to only single type of data and there are few efforts that consider the mixed-type of data when modelling (Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

Wang

Zhao

2019

Knowledge-Based Systems

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The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those models might be constrained to a certain type of data. Aiming at aggregating differently sourced information and offering type-free capability to existing models, a framework for predicting stock market of scenarios with mixed data, including scalar data, compositional data (pie-like) and functional data (curvelike), is established. The presented framework is model-independent, as it serves like an interface to multiple types of data and can be combined with various prediction models. And it is proved to be effective through numerical simulations. Regarding to price prediction, we incorporate the trading volume (scalar data), intraday return series (functional data), and investors' emotions from social media (compositional data) through the framework to competently forecast whether the market goes up or down at opening in the next day. The strong explanatory power of the framework is further demonstrated. Specifically, it is found that the intraday returns impact the following opening prices differently between bearish market and bullish market. And it is not at the beginning of the bearish market but the subsequent period in which the investors' "fear" comes to be indicative. The framework would help extend existing prediction models easily to scenarios with multiple types of data and shed light on a more systemic understanding of the stock market.

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“…Since the first special issue on robust analysis of complex data was published in CSDA (Croux et al, 2013) four years ago, the journal continued to attract a considerable body of work on different aspects of robustness in complex data analysis. These include Alfons et al (2016), Cui et al (2016), Hämäläinen (2016), Kirschstein et al (2016), Martinez andGray (2016), Salibián-Barrera et al (2016) and Tarr et al (2016), the seven articles in a special issue on advances in data mining and robust statistics as well as articles in regular issues such as Atkinson et al (2016), Boente and Pardo-Fernández (2016), Chee and Wang (2016), Cheng (2016), García-Escudero et al (2016), Gutiérrez et al (2016), Huang et al (2016), Leung et al (2016), Li et al (2016), Mount et al (2016), Xiang et al (2016), Aeberhard et al (2017), Agostinelli et al (2017), Bianco and Spano (2017), Kaffine and Davis (2017), Maronna andYohai (2017), Moradi Rekabdarkolaee et al (2016), Serfling and Wijesuriya (2017) and Song et al (2017), to just focus on publications since 2016. We encourage all readers to continue to choose CSDA as one of their main outlets to share their latest achievements in the various important areas of robust statistics.…”

mentioning

confidence: 99%

2nd special issue on robust analysis of complex data

Croux

Ronchetti

Salibián‐Barrera

et al. 2017

Computational Statistics & Data Analysis

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2nd special issue on robust analysis of complex data When we embarked to invite submissions for this 2nd special issue on robust analysis of complex data, we did so with the following call: Nowadays we are often confronted with large data sets in high dimensions. These new types of data have led to the emergence and use of complex models such as graphical models, models for complex correlation structures and models for functional data for instance. These complex structures pose many challenges. Specific ally, when many measurements on several variables are recorded, it becomes more likely that not all of these measurements are recorded with high accuracy. This may result in data of uneven quality that contains gross errors and other anomalies that need to be taken into account. Therefore, there is a need for robust procedures that can reliably analyze large data sets containing outliers and other data contamination. This special issue will focus mainly on computationally efficient, robust procedures to analyze such complex data sets. This call resulted in 37 submissions, from which five high quality papers have been selected for inclusion in this special issue. We greatly acknowledge the help of the CSDA co-editors to handle these papers and we thank all anonymous referees for their valuable comments, honest opinions and constructive suggestions. Dhaene and Zhu ( 2017) study the robustness properties of median-based estimators for the AR(1) coefficient in different types of additive outliers scenarios for panel data when the number of time-points is growing to infinity while the number of observed panels is fixed. They do so through showing that the influence function is bounded when outliers occur independently over time or are patched additive, i.e. they occur in random sequences of length k. Bali and Boente (2017) consider robust principal component estimation of several populations of functional data. They adapt methods from one population functional data, instead of estimating the covariance operator for each of the populations separately, Bali and Boente (2017) propose to use robust projection-pursuit estimators for the common directions under a common principal component model instead. The benefit is that considerably fewer parameters need to be estimated. A particular highlight of the article is that Bali and Boente (2017) extend the definition of a functional common principal component model to the situation in which the covariance operator does not exist or when the underlying distribution is not elliptical, and show how this still ensures Fisher-consistency. Chiancone et al. (2017) show how to robustify Sliced Inverse Regression (SIR), which has been shown in Cook (2007) to correspond to the maximum likelihood of an inverse regression model with Gaussian errors, by extending its inverse regression formulation to non-Gaussian errors with heavy tailed distributions. In contrast to alternative robust versions of SIR, Chiancone et al. (2017) do not replace the standard SIR estimators by robust versions bu...

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Robust shrinkage estimation and selection for functional multiple linear model through LAD loss

Cited by 15 publications

References 30 publications

Variable Selection for Multiple Function-on-Function Linear Regression

Variable Selection for Multiple Function-on-Function Linear Regression

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

2nd special issue on robust analysis of complex data

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