arXiv subjects

Sahoko Ishida

Publications and source records attributed to Sahoko Ishida.

7 recordsLinked to original sources

arXiv2026

Filling survey gaps in food security monitoring with spatio-temporal additive Gaussian process models

Ensuring food security across all regions of a country requires continuous monitoring, yet household surveys often leave significant spatio-temporal gaps due to resource constraints and operational priorities. In this paper, we propose a spatio-temporal additive Gaussian process model to estimate sub-national food security time series by regions. To address the computational cost of Gaussian process models, we exploit Kronecker structure of the spatio-temporal covariance matrix for scalable inference. We evaluate the proposed approach on food security survey data from Nigeria and Chad comparing it against other statistical and machine learning models and show how our proposal achieves better accuracy while retaining reliable uncertainty, especially when covariates are informative. We further apply the model to generate estimates for Nigerian states not covered by the survey, demonstrating its operational value for filling geographic gaps in food security monitoring.

stat.AP↗

arXiv2026

Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis

Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than overall scores. Drawing conclusions about learning from these data requires a model that links responses to latent skills and tracks how mastery changes over time. When the skills measured by each item are unknown, the analyst must decide whether to estimate this structure, the Q-matrix, jointly with the learning process, or to establish it first and study learning afterwards. We show that this decision can change substantive conclusions about how learners develop. Using dynamic cognitive diagnostic models, we analyse data from two reading games measuring vocabulary and comprehension from Grade 2 to Grade 3, with item-text embeddings providing prior information for the unknown Q-matrix. A joint analysis and a bias-corrected stepwise analysis agree that most learners move toward mastering both skills, but disagree about how many remain only partially proficient at Grade 3, changing how reading progress would be reported. A simulation study identifies when the two analyses diverge and shows that joint analysis is more reliable when the item-skill structure is uncertain and the item pool changes between grades. We provide R code for both analyses.

cs.LG↗

arXiv2026

Mapping Subnational Vulnerability to Inadequate Micronutrient Intake using a Bayesian Small Area Estimation Framework

Inadequate dietary micronutrient intake is a significant risk factor for deficiency and remains a major global health challenge. Nutrition programmes and interventions are most effective when targeted to populations at greatest risk. Household Consumption and Expenditure Surveys (HCES) are a widely available source of dietary data; however, they are often not powered for estimation below the first administrative level, limiting their utility for geographically targeted interventions. To address this, we applied Bayesian Small Area Estimation (SAE) methods to estimate the prevalence of apparent inadequate intake at the second administrative level. Three approaches were considered: a cluster level Beta binomial model and two area level models (mean smoothing and joint smoothing). Models were evaluated using a Rwanda HCES survey that supports inference at this scale. All models were implemented in a fully Bayesian framework to propagate uncertainty. Simulation results in Rwanda showed that the cluster level Beta binomial model achieved the strongest performance, while the area level joint smoothing model was the most reliable alternative among models accounting for survey design. Based on these results, models were applied to Senegal and Nigeria. In Senegal, second administrative level estimates captured meaningful subnational variation, reduced uncertainty relative to direct estimates, and remained consistent with first administrative level benchmarks. In Nigeria, despite smaller sample sizes and survey design constraints, modelled estimates reduced extreme uncertainty and showed good agreement with first administrative level estimates. This study demonstrates that Bayesian SAE methods can be applied to HCES data to generate reliable fine scale estimates of inadequate micronutrient intake, supporting localised nutrition interventions.

stat.AP↗

arXiv2026

NLP-Informed Dynamic Cognitive Diagnosis Modelling

Digital learning platforms are increasingly used to support reading development while generating rich log files and item-level textual content. Using these data, this study proposes a dynamic cognitive diagnostic modelling (CDM) framework that incorporates text-derived semantic information to inform the estimation of the Q-matrix. We construct item-level semantic representations of question text and response options, and use these representations to define an informative prior on the Q-matrix. This approach treats text-derived signals as proxies for item complexity and cognitive demands, guiding the item-skill mapping in a data-driven manner. The proposed framework jointly estimates latent skill mastery profiles, item parameters, and transition dynamics over time within a Bayesian framework. We apply the model to data from Boost Reading, a digital reading supplement, focusing on students' vocabulary and comprehension skill development. We compare the proposed framework with a baseline model without any text information and show that the text-derived prior can improve Q-matrix recovery, particularly in settings where response data alone provide limited identification, as well as other model parameters for varying scenarios. This study provides a novel integration of natural language processing and dynamic CDMs, offering a data-driven approach to modelling skill acquisition and item-skill relationships in digital learning environments.

stat.ME↗

arXiv2025

Real-time small area estimation of food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification

Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key objective of the 2030 Agenda for Sustainable Development. Mobile phone surveys provide a scalable and temporally rich data source that can be tailored to different administrative levels. However, due to cost and operational constraints, maintaining high-frequency data collection while ensuring representativeness at lower administrative levels is often infeasible. We propose a joint multilevel regression and poststratification (jMRP) approach that combines high-frequency and up-to-date mobile phone survey data, designed for higher administrative levels, with an annual face-to-face survey representative at lower levels to produce reliable food security estimates at spatially and temporally finer scales than those originally targeted by the surveys. This methodology accounts for systematic differences in survey responses due to modality and socio-economic characteristics, reducing both sampling and modality bias. We implement the approach in a fully Bayesian manner to quantify uncertainty. We demonstrate the effectiveness of our method using data from Zimbabwe, thus offering a cost-effective solution for real-time monitoring and strengthening decision-making in resource-constrained settings.

stat.AP↗

arXiv2024

Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing

Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.

cs.CV↗

arXiv2023

Hierarchical additive interaction modelling with Gaussian process prior and its efficient implementation for multidimensional grid data

Additive Gaussian process (GP) models offer flexible tools for modelling complex non-linear relationships and interaction effects among covariates. While most studies have focused on predictive performance, relatively little attention has been given to identifying the underlying interaction structure, which may be of scientific interest in many applications. In practice, the use of additive GP models in this context has been limited by the cubic computational cost and quadratic storage requirements of GP inference. This paper presents a fast hierarchical additive interaction GP model for multi-dimensional grid data. A hierarchical ANOVA decomposition kernel forms the foundation of our model, which incorporate main and interaction effects under the principle of marginality. Kernel centring ensures identifiability and provides a unique, interpretable decomposition of lower- and higher-order effects. For datasets forming a multi-dimensional grid, efficient implementation is achieved by exploiting the Kronecker product structure of the covariance matrix. Our contribution is to extend Kronecker-based computation to handle any interaction structure within the proposed class of hierarchical additive GP models, whereas previous methods were limited to separable or fully saturated cases. The benefits of the proposed approach are demonstrated through simulation studies and an application to high-frequency nitrogen dioxide concentration data in London.

stat.ME↗