arXiv subjects

Jun-ichi Takeshita

Publications and source records attributed to Jun-ichi Takeshita.

7 recordsLinked to original sources

arXiv2026

Statistical evaluation of measurement precision in linear dose-response relationships via interlaboratory studies

This paper proposes a framework for evaluating the statistical precision of measurement methods from interlaboratory studies where the outcome is a dose-response relationship summarized by a regression line. For such measurement methods, where a linear mixed-effects model is applied that allows laboratories to differ in both baseline level and dose-response slope, we define precision evaluation metrics specified in ISO 5725, repeatability and between-laboratory variances. These are method-level precision metrics, and the latter are constructed as design-averaged dose-specific between-laboratory variances over the dose levels and the participating laboratories. For fully balanced designs with common dose levels and equal replication, we obtain an exact decomposition of the total sum of squares, closed-form analysis of variance (ANOVA) estimators of the precision variances, and three associated $F$-tests targeting (i) the overall dose-response trend, (ii) homogeneity of intercepts, and (iii) homogeneity of slopes across laboratories. This formulation enables precision to be quantified and estimated directly and supports an evaluation of whether between-laboratory discrepancies are caused primarily by baseline shifts or by differences in sensitivity, in contrast to fixed-effect comparisons that only detect the presence of differences. Furthermore, we analyze data obtained from an interlaboratory study on observations in bronchoalveolar lavage fluid from experiments involving the intratracheal administration of nanomaterials to rats, using the proposed method as a case study.

stat.AP↗

arXiv2026

Bootstrap-based estimation and inference for measurement precision under ISO 5725

The ISO 5725 series frames interlaboratory precision through repeatability, between-laboratory, and reproducibility variances, yet practical guidance on deploying bootstrap methods within this one-way random-effects setting remains limited. We study resampling strategies tailored to ISO 5725 data and extend a bias-correction idea to obtain simple adjusted point estimators and confidence intervals for the variance components. Using extensive simulations that mirror realistic study sizes and variance ratios, we evaluate accuracy, stability, and coverage, and we contrast the resampling-based procedures with ANOVA-based estimators and common approximate intervals. The results yield a clear division of labor: adjusted within-laboratory resampling provides accurate and stable point estimation in small-to-moderate designs, whereas a two-stage strategy-resampling laboratories and then resampling within each-paired with bias-corrected and accelerated intervals offers the most reliable (near-nominal or conservative) confidence intervals. Performance degrades under extreme designs, such as very small samples or dominant between-laboratory variation, clarifying when additional caution is warranted. A case study from an ISO 5725-4 dataset illustrates how the recommended procedures behave in practice and how they compare with ANOVA and approximate methods. We conclude with concrete guidance for implementing resampling-based precision analysis in interlaboratory studies: use adjusted within-laboratory resampling for point estimation, and adopt the two-stage strategy with bias-corrected and accelerated intervals for interval estimation.

stat.AP↗

arXiv2024

A model and method for analyzing the precision of binary measurement methods based on beta-binomial distributions, and related statistical tests

This study developed a new statistical model and method for analyzing the precision of binary measurement methods from collaborative studies. The model is based on beta-binomial distributions. In other words, it assumes that the sensitivity of each laboratory obeys a beta distribution, and the binary measured values under a given sensitivity follow a binomial distribution. We propose the key precision measures of repeatability and reproducibility for the model, and provide their unbiased estimates. Further, through consideration of a number of statistical test methods for homogeneity of proportions, we propose appropriate methods for determining laboratory effects in the new model. Finally, we apply the results to real-world examples in the fields of food safety and chemical risk assessment and management.

stat.AP↗

arXiv2022

A remark on statistics for detecting laboratory effects in ORDANOVA

The present study defines a new statistic for detecting laboratory effects in the analysis of ordinal variation (ORDANOVA). The ORDANOVA is an analysis method similar to one-way analysis of variance for analysing ordinal data obtained from interlaboratory comparison studies. In this paper, we present an approximate continuous distribution for the new statistic for the case of an arbitrary number of ordinal levels, and we demonstrate that $alpha$-percentiles of the distribution are suitable criteria for conducting statistical tests. In addition, a real example involving data from an interlaboratory comparison study is analysed using the proposed statistic.

stat.AP↗

arXiv2021

Catastrophic failure and cumulative damage models involving two types of extended exponential distributions

The present study supposes a single unit and investigates cumulative damage and catastrophic failure models for the unit, in situations where the interarrival times between the shocks, and the magnitudes of the shocks, involve two different stochastic processes. In order to consider two essentially different stochastic processes, integer gamma and Weibull distributions are treated as distributions with two parameters and extensions of exponential distributions. With respect to the cumulative damage models, under the assumption that the interarrival times between shocks follow exponential distributions, the case in which the magnitudes of the shocks follow integer gamma distributions is analyzed. With respect to the catastrophic failure models, the respective cases in which the interarrival times between shocks follow integer gamma and Weibull distributions are discussed. Finally, the study provides some characteristic values for reliability in such models.

math.PR↗

arXiv2021

Graph reliability evaluation via random $K$-out-of-$N$ systems

The present study was concerned with network failure problems for simple connected undirected graphs. A connected graph becomes unconnected through edge failure, under the assumptions that only edges can fail and each edge has an identical failure distribution. The main purpose of the present study was to show recurrent relations with respect to the number of edges in graph generation procedures. To this end, simple connected undirected graphs were correlated to random $K$-out-of-$N$ systems, and key features of such systems were applied. In addition, some simple graph cases and examples were analyzed.

math.PR↗

arXiv2021

Classification of chemical compounds based on the correlation between \textit{in vitro} gene expression profiles

Toxicity evaluation of chemical compounds has traditionally relied on animal experiments;however, the demand for non-animal-based prediction methods for toxicology of compounds is increasing worldwide. Our aim was to provide a classification method for compounds based on \textit{in vitro} gene expression profiles. The \textit{in vitro} gene expression data analyzed in the present study was obtained from our previous study. The data concerned nine compounds typically employed in chemical management.We used agglomerative hierarchical clustering to classify the compounds;however, there was a statistical difficulty to be overcome.We needed to properly extract RNAs for clustering from more than 30,000 RNAs. In order to overcome this difficulty, we introduced a combinatorial optimization problem with respect to both gene expression levels and the correlation between gene expression profiles. Then, the simulated annealing algorithm was used to obtain a good solution for the problem. As a result, the nine compounds were divided into two groups using 1,000 extracted RNAs. Our proposed methodology enables read-across, one of the frameworks for predicting toxicology, based on \textit{in vitro} gene expression profiles.

stat.AP↗