Australian & New Zealand Journal of Statistics最新文献

Density function estimation is a cornerstone of statistical analysis. This paper focuses on the generalised edge frequency polygon estimator, establishing its asymptotic normality for identically distributed $��\alpha �$-mixing random variables. This finding complements the asymptotic theory outlined by Dong and Zheng (2001. Generalized edge frequency polygon for density estimation. Statistics and Probability Letters, 55, 137–145). Theoretical results are substantiated through simulations that assess finite-sample performance and an analysis of a real-world dataset.

R and Python are the two dominant language tools for data science today. Which one is better, and in what senses? This paper explores such questions, in terms of aspects such as learning curve, clarity of expression, coding philosophy, high-performance computing capability and so on. Also, the article treats base-R and the tidyverse as two separate ‘dialects’ of R, so that the above comparisons are in many cases tripartite in nature.

Density function estimation is a cornerstone of statistical analysis. This paper focuses on the generalised edge frequency polygon estimator, establishing its asymptotic normality for identically distributed $��\alpha �$-mixing random variables. This finding complements the asymptotic theory outlined by Dong and Zheng (2001. Generalized edge frequency polygon for density estimation. Statistics and Probability Letters, 55, 137–145). Theoretical results are substantiated through simulations that assess finite-sample performance and an analysis of a real-world dataset.

Model averaging for categorical response variables has gained a lot of attention in recent years. To further improve the prediction accuracy, we present a partially linear multinomial logit model averaging (PLMLMA) technique. Our candidate models are built by selecting each continuous covariate in turn as the index variable of the non-parametric function, thus avoiding both the artificial selection of index variables and the curse of dimensionality in estimation. The model averaging weights are determined by minimising the Kullback-Leibler (KL) loss. We demonstrate asymptotic optimality by showing that the KL loss between the true model and the model where the log-odds ratio is estimated by the ‘working’ log-odds ratio is asymptotically equivalent to that of the best but impractical model averaging estimator. Furthermore, we establish the convergence rate of the weight estimator without assuming that the true model is included among the candidate models. The superior performance of the proposed method is evidenced by lower mean squared error (MSE) and higher hit rate (HR) in simulations, outperforming various competitors. We also apply our method to wheat variety classification to illustrate the merits of PLMLMA.

Linear model prediction with a large number of potential predictors is both statistically and computationally challenging. The traditional approaches are largely based on shrinkage selection/estimation methods, which are applicable even when the number of potential predictors is (much) larger than the sample size. A situation of the latter scenario occurs when the candidate predictors involve many binary indicators corresponding to categories of some categorical predictors as well as their interactions. We propose an alternative approach to the shrinkage prediction methods in such a case based on mixed model prediction, which effectively treats combinations of the categorical effects as random effects. We establish theoretical validity of the proposed method and demonstrate empirically its advantage over the shrinkage methods. We also develop measures of uncertainty for the proposed method and evaluate their performance empirically. A real-data example is considered.

A spatial point pattern is a dataset representing the observed locations of things or events, such as disease cases, distant galaxies, trees, crimes, earthquakes or road accidents. The stochastic mechanism that generated the data is called a spatial point process. The statistical analysis of such data has been completely transformed by the availability of R. The R environment has enabled fundamental methodological research to proceed hand in hand with software development, leading to substantial advances in statistical methodology for spatial point processes which were immediately applicable to real data. This article is a broad historical account of the breakthroughs in statistical methodology for spatial point processes that were facilitated by R software, specifically the authors' package spatstat. It focuses on spatial point process modelling, model-fitting, model diagnostics, challenges to statistical inference, lessons to be learned and new challenges for future research.

This article focuses on community detection in multi-layer-directed degree-corrected block models. A spectral co-clustering method building upon a bias-adjusted aggregation matrix is proposed to identify the community memberships. We theoretically demonstrate the efficacy of our approach by controlling the proportion of misclustered nodes. Both simulations and real-data analysis provide empirical evidence supporting the effectiveness of our proposed methodology.

The tidyverse is a popular meta-package comprising several core R packages to aid in various data science tasks, including data import, manipulation and visualisation. Although functionalities offered by the tidyverse can generally be replicated using other packages, its widespread adoption in both teaching and practice indicates there are factors contributing to its preference, despite some debate over its usage. This suggests that particular aspects, such as interface design, may play a significant role in its selection. Examining the interface design can potentially reveal aspects that aid the design process for developers. While Tidyverse has been lauded for adopting a user-centred design, arguably some elements of the design focus on the work domain instead of the end user. We examine the Tidyverse interface design via the lens of human–computer interaction, with an emphasis on data visualisation and data wrangling, to identify factors that might serve as a model for developers designing their packages. We recommend that developers adopt an iterative design that is informed by user feedback, analysis and complete coverage of the work domain, and ensure perceptual visibility of system constraints and relationships.