2 citations · 3 across the 7 of their papers we have counts for
10 papers
Variable-Projection Sparse Functional Principal Component Analysis: Interpretable Functional Dimensionality Reduction with Applications to Raman Spectral Data
Han Ying Lim, Dharini Pathmanathan, Philipp Otto +1
Functional principal component analysis (FPCA) provides low-rank representations of functional data but generally produces dense components, making it difficult to identify the loc…
Neighbourhood-Based Generalized Dynamic Principal Components for Spatial Functional Data
Dharini Pathmanathan, Teck Xiang Seow, Philipp Otto +1
Regular-grid spatial functional datasets arise naturally in gridded environmental, oceanographic, climate, and remote-sensing applications, where each spatial location is associate…
Reframing Three-Dimensional Morphometrics Through Functional Data Innovations
Aneesha Balachandran Pillay, Issa-Mbenard Dabo, Sophie Dabo-Niang +1
This study innovates geometric morphometrics by incorporating functional data analysis, the square-root velocity function (SRVF), and arc-length parameterisation for 3D morphometri…
Functional regression with randomized signatures: An application to age-specific mortality rates
Zhong Jing Yap, Dharini Pathmanathan, Sophie Dabo-Niang
We propose a novel extension of the Hyndman-Ullah (HU) model to forecast mortality rates by integrating randomized signatures, referred to as the HU model with randomized signature…
Compositional data analysis for modelling and forecasting mortality using the α-transformation
Han Ying Lim, Dharini Pathmanathan, Sophie Dabo-Niang
Mortality forecasting is crucial for demographic planning and actuarial studies, especially for projecting population ageing and longevity risk. Classical approaches largely rely o…
Spatial Principal Component Analysis and Moran Statistics for Multivariate Functional Areal Data
Dharini Pathmanathan, Issa-Mbenard Dabo, Tzung Hsuen Khoo +2
The paper introduces a multivariate functional areal spatial principal component analysis (mfasPCA) framework, together with multivariate functional Moran's I statistics, to enable…
Generalized dynamic functional principal component analysis
Tzung Hsuen Khoo, Issa-Mbenard Dabo, Dharini Pathmanathan +1
In this paper, we explore dimension reduction for functional time series. We propose a generalized dynamic functional principal component analysis (GDFPCA) which does not rely on s…
Forecasting mortality rates with functional signatures
Zhong Jing Yap, Dharini Pathmanathan, Sophie Dabo-Niang
This study introduces an innovative methodology for mortality forecasting, which integrates signature-based methods within the functional data framework of the Hyndman-Ullah (HU) m…
A Markov-switching spatio-temporal ARCH model
Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto +1
Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARC…
A unified approach for morphometrics and functional data analysis with machine learning for craniodental shape quantification in shrew species
Aneesha Balachandran Pillay, Dharini Pathmanathan, Sophie Dabo-Niang +2
This work proposes a functional data analysis approach for morphometrics with applications in classifying three shrew species (S. murinus, C. monticola and C. malayana) based on th…