Envelopes: A new chapter in partial least squares regression

Cook, R. Dennis; Forzani, Liliana

doi:10.1002/cem.3287

Cited by 15 publications

(12 citation statements)

References 56 publications

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“…For example, the active vector for a linear problem is simply the gradient

\boldsymbol{b}

, regardless of the particular distribution assigned to the inputs. Perhaps useful connections could be made to dimension reduction techniques known as Envelope Methods and Partial Least Squares [30].…”

Section: Discussionmentioning

confidence: 99%

Driving mode analysis—How uncertain functional inputs propagate to an output

Vander Wiel,

Grosskopf,

Michaud

et al. 2023

Statistical Analysis

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Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.

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“…For example, the active vector for a linear problem is simply the gradient

\boldsymbol{b}

Section: Discussionmentioning

confidence: 99%

Driving mode analysis—How uncertain functional inputs propagate to an output

Vander Wiel,

Grosskopf,

Michaud

et al. 2023

Statistical Analysis

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“…Some of these claims have already been addressed in prior research (e.g. Cook and Forzani, 2020; Henseler et al. , 2014; Rigdon, 2012; Sarstedt et al.…”

Section: Introductionmentioning

confidence: 89%

Going beyond the untold facts in PLS–SEM and moving forward

Hair,

Sarstedt,

Ringle

et al. 2024

EJM

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Purpose This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities. Findings Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy. Research limitations/implications We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications. Practical implications All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field. Originality/value This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

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“…Sejauh mana faktor-faktor ekonomi dan sosial seperti UHH, PDRB per kapita ADHB, dan TPAK, serta bagaimana peran RLS sebagai penghubung antara faktor-faktor tersebut terhadap IPM di Sulawesi Selatan dapat digunakan metode analisis jalur. Metode tersebut digunakan sebagai cara standar untuk merepresentasikan teori dalam ilmu sosial [8]. Model regresi yang diperluas dengan menggunakan variabel perantara sehingga sistem hubungan sebab akibat (kausal) memiliki 2 jenis variabel, yaitu variabel eksogen dengan simbol 𝑋 1 , 𝑋 2 , … , 𝑋 𝑘 dan variabel endogen dengan simbol 𝑌 1 , 𝑌 2 , … .…”

Section: Pendahuluanunclassified

Path Analysis of Influence of Economic and Social Factors on the Human Development Index in South Sulawesi in 2022

Parapa,

Chesynanda,

Siswanto

et al. 2023

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The Human Development Index (HDI) serves as an indicator for assessing socio-economic development in a region. Each area strives to improve its HDI by considering the factors that influence it in that specific region. This research aims to identify the direct and indirect influences of economic and social factors, such as Life Expectancy (LE), Gross Regional Domestic Product per capita (GRDPpc), Labor Force Participation Rate (LFPR) through Average Years of Schooling (AYS) on the HDI in South Sulawesi in 2022. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of South Sulawesi Province in 2022. The method applied in this research is a path analysis that examines the relationships between variables, both direct and indirect influences. The research results show that in the equation of sub-structure 1, LE and GRDP per capita ADHB have a direct influence on AYS, while LFPR does not have a direct impact on AYS. The magnitude of the influence of variables in sub-structure 1 is 53%. In the equation of sub-structure 2, LE, GRDP per capita ADHB, LFPR, and AYS have a significant direct impact on HDI. Additionally, LE and GRDP per capita ADHB have an indirect influence through AYS on HDI. The magnitude of the influence of variables in sub-structure 2 is 93.5%. Therefore, the variables that have both direct and indirect effects on HDI through AYS are LE and GRDP per capita ADHB.

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Envelopes: A new chapter in partial least squares regression

Cited by 15 publications

References 56 publications

Driving mode analysis—How uncertain functional inputs propagate to an output

Driving mode analysis—How uncertain functional inputs propagate to an output

Going beyond the untold facts in PLS–SEM and moving forward

Path Analysis of Influence of Economic and Social Factors on the Human Development Index in South Sulawesi in 2022

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