Concept drift estimation with graphical models

Riso, Luigi; Guerzoni, Marco

doi:10.1016/j.ins.2022.05.056

Information Sciences

2022

DOI: 10.1016/j.ins.2022.05.056

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Concept drift estimation with graphical models

Luigi Riso

Marco Guerzoni

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Cited by 8 publications

(1 citation statement)

References 40 publications

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“…It is important to note that we cannot take the magnitude of these links into account, but only their presence or absence, which defines the structure of the tree itself(Riso and Guerzoni, 2022).10 In line with the recent literature (see Section 2). 11 This outcome can be due to the very small incidence of innovative firms within our population.…”

mentioning

confidence: 86%

Was Robert Gibrat Right? A Test Based on the Graphical Model Methodology

Guerzoni¹,

Riso²,

Vivarelli

2023

SSRN Journal

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mentioning

confidence: 86%

Was Robert Gibrat Right? A Test Based on the Graphical Model Methodology

Guerzoni¹,

Riso²,

Vivarelli

2023

SSRN Journal

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Was Robert Gibrat right? A test based on the graphical model methodology

Guerzoni,

Riso,

Vivarelli

2024

Small Bus Econ

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Using both regression analysis and an unsupervised graphical model approach (never applied before to this issue), we confirm the rejection of Gibrat’s Law (stating that a firm’s growth is independent of that firm’s initial size) when our firm-level data are considered over the entire investigated period, while the opposite is true when we allow for market selection; indeed, the growth behavior of the surviving most efficient firms is in line with Gibrat’s Law. This evidence reconciles early and current literature and may have interesting implications in terms of both theoretical research and policy suggestions regarding subsidies to small firms, which do not necessarily grow faster than their larger counterparts.

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Time varying effects in survival analysis: a novel data-driven method for drift identification and variable selection

Babutsidze,

Guerzoni,

Riso

2024

Eurasian Bus Rev

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In this paper we address the problem of survival models when high-dimensional panel data are available. We discuss two related issues: The first one concerns the issue of variable selection and the second one deals with the stability over time of such a selection, since presence of time dimension in survival data requires explicit treatment of evolving socio-economic context. We show how graphical models can serve two purposes. First they serve as the input for a first algorithm to to assess the temporal stability of the data: Secondly, allow the deployment of a second algorithm which partially automates the process of variable selection, while retaining the option to incorporate domain expertise in the process of empirical model-building. To put our proposed methodology to the test, we utilize a dataset comprising Italian firms funded in 2009 and we study the survival of these entities over the period of 10 years. In addition to revealing significant volatility in the set of variables explaining firm exit over the years, our novel methodology enables us to offer a more nuanced perspective than the conventional one regarding the critical roles played by traditional variables such as industrial sector, geographical location, and innovativeness in firm survival.

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Concept drift estimation with graphical models

Cited by 8 publications

References 40 publications

Was Robert Gibrat Right? A Test Based on the Graphical Model Methodology

Was Robert Gibrat Right? A Test Based on the Graphical Model Methodology

Was Robert Gibrat right? A test based on the graphical model methodology

Time varying effects in survival analysis: a novel data-driven method for drift identification and variable selection

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