343 research outputs found
Adaptive Huber Regression
Big data can easily be contaminated by outliers or contain variables with
heavy-tailed distributions, which makes many conventional methods inadequate.
To address this challenge, we propose the adaptive Huber regression for robust
estimation and inference. The key observation is that the robustification
parameter should adapt to the sample size, dimension and moments for optimal
tradeoff between bias and robustness. Our theoretical framework deals with
heavy-tailed distributions with bounded -th moment for any . We establish a sharp phase transition for robust estimation of regression
parameters in both low and high dimensions: when , the estimator
admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on
the data, while only a slower rate is available in the regime .
Furthermore, this transition is smooth and optimal. In addition, we extend the
methodology to allow both heavy-tailed predictors and observation noise.
Simulation studies lend further support to the theory. In a genetic study of
cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown
to be more robust and predictive.Comment: final versio
A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach
High dimensional hypothesis test deals with models in which the number of
parameters is significantly larger than the sample size. Existing literature
develops a variety of individual tests. Some of them are sensitive to the dense
and small disturbance, and others are sensitive to the sparse and large
disturbance. Hence, the powers of these tests depend on the assumption of the
alternative scenario. This paper provides a unified framework for developing
new tests which are adaptive to a large variety of alternative scenarios in
high dimensions. In particular, our framework includes arbitrary hypotheses
which can be tested using high dimensional -statistic based vectors. Under
this framework, we first develop a broad family of tests based on a novel
variant of the -norm with . We then combine these
tests to construct a data-adaptive test that is simultaneously powerful under
various alternative scenarios. To obtain the asymptotic distributions of these
tests, we utilize the multiplier bootstrap for -statistics. In addition, we
consider the computational aspect of the bootstrap method and propose a novel
low-cost scheme. We prove the optimality of the proposed tests. Thorough
numerical results on simulated and real datasets are provided to support our
theory
Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument
This study examines the relationship between student perceptions and their
intention to use generative AI in higher education. Drawing on Expectancy-Value
Theory (EVT), a questionnaire was developed to measure students' knowledge of
generative AI, perceived value, and perceived cost. A sample of 405 students
participated in the study, and confirmatory factor analysis was used to
validate the constructs. The results indicate a strong positive correlation
between perceived value and intention to use generative AI, and a weak negative
correlation between perceived cost and intention to use. As we continue to
explore the implications of generative AI in education and other domains, it is
crucial to carefully consider the potential long-term consequences and the
ethical dilemmas that may arise from widespread adoption
Distributed Adaptive Huber Regression
Distributed data naturally arise in scenarios involving multiple sources of
observations, each stored at a different location. Directly pooling all the
data together is often prohibited due to limited bandwidth and storage, or due
to privacy protocols. This paper introduces a new robust distributed algorithm
for fitting linear regressions when data are subject to heavy-tailed and/or
asymmetric errors with finite second moments. The algorithm only communicates
gradient information at each iteration and therefore is
communication-efficient. Statistically, the resulting estimator achieves the
centralized nonasymptotic error bound as if all the data were pooled together
and came from a distribution with sub-Gaussian tails. Under a finite
-th moment condition, we derive a Berry-Esseen bound for the
distributed estimator, based on which we construct robust confidence intervals.
Numerical studies further confirm that compared with extant distributed
methods, the proposed methods achieve near-optimal accuracy with low
variability and better coverage with tighter confidence width.Comment: 29 page
How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?
Although the open source model bears many advantages in software development,
open source projects are always hard to sustain. Previous research on open
source sustainability mainly focuses on projects that have already reached a
certain level of maturity (e.g., with communities, releases, and downstream
projects). However, limited attention is paid to the development of
(sustainable) open source projects in their infancy, and we believe an
understanding of early sustainability determinants is crucial for project
initiators, incubators, newcomers, and users.
In this paper, we aim to explore the relationship between early participation
factors and long-term project sustainability. We leverage a novel methodology
combining the Blumberg model of performance and machine learning to predict the
sustainability of 290,255 GitHub projects. Specificially, we train an XGBoost
model based on early participation (first three months of activity) in 290,255
GitHub projects and we interpret the model using LIME. We quantitatively show
that early participants have a positive effect on project's future sustained
activity if they have prior experience in OSS project incubation and
demonstrate concentrated focus and steady commitment. Participation from
non-code contributors and detailed contribution documentation also promote
project's sustained activity. Compared with individual projects, building a
community that consists of more experienced core developers and more active
peripheral developers is important for organizational projects. This study
provides unique insights into the incubation and recognition of sustainable
open source projects, and our interpretable prediction approach can also offer
guidance to open source project initiators and newcomers.Comment: The 31st ACM Joint European Software Engineering Conference and
Symposium on the Foundations of Software Engineering (ESEC/FSE 2023
Personalized First Issue Recommender for Newcomers in Open Source Projects
Many open source projects provide good first issues (GFIs) to attract and
retain newcomers. Although several automated GFI recommenders have been
proposed, existing recommenders are limited to recommending generic GFIs
without considering differences between individual newcomers. However, we
observe mismatches between generic GFIs and the diverse background of
newcomers, resulting in failed attempts, discouraged onboarding, and delayed
issue resolution. To address this problem, we assume that personalized first
issues (PFIs) for newcomers could help reduce the mismatches. To justify the
assumption, we empirically analyze 37 newcomers and their first issues resolved
across multiple projects. We find that the first issues resolved by the same
newcomer share similarities in task type, programming language, and project
domain. These findings underscore the need for a PFI recommender to improve
over state-of-the-art approaches. For that purpose, we identify features that
influence newcomers' personalized selection of first issues by analyzing the
relationship between possible features of the newcomers and the characteristics
of the newcomers' chosen first issues. We find that the expertise preference,
OSS experience, activeness, and sentiment of newcomers drive their personalized
choice of the first issues. Based on these findings, we propose a Personalized
First Issue Recommender (PFIRec), which employs LamdaMART to rank candidate
issues for a given newcomer by leveraging the identified influential features.
We evaluate PFIRec using a dataset of 68,858 issues from 100 GitHub projects.
The evaluation results show that PFIRec outperforms existing first issue
recommenders, potentially doubling the probability that the top recommended
issue is suitable for a specific newcomer and reducing one-third of a
newcomer's unsuccessful attempts to identify suitable first issues, in the
median.Comment: The 38th IEEE/ACM International Conference on Automated Software
Engineering (ASE 2023
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