Article
Psychology, Multidisciplinary
Daniel Lakens
Summary: Due to the strong reliance on p values in scientific literature, some researchers argue for moving beyond p values and embracing practical alternatives. It is suggested that statisticians should teach researchers what questions they can ask rather than telling them what they want to know, and including minimum-effect tests and equivalence tests in statistical toolbox may greatly improve the questions researchers ask. Developing better evidence-based education and user-centered statistical software to prevent misinterpretation of p values is seen as a top priority.
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE
(2021)
Article
Engineering, Electrical & Electronic
Mohsen Moradi
Summary: This paper introduces a sequential decoding metric function that improves error-correction performance of PAC codes with enhanced computational efficiency. The proposed metric function results in comparable decoding complexity between PAC codes and conventional convolutional codes. Search-limited sequential decoding achieves error-correction performance close to polar codes with considerably less computational complexity.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Sh. Rezapour, S. Kumar, M. Q. Iqbal, A. Hussain, S. Etemad
Summary: In this research, the conditions for the existence of solutions to two abstract multi-term sequential boundary value problems defined with Caputo derivatives are investigated. Hypotheses on the existing single-valued and multi-valued functions in the given fractional differential equation and inclusion are considered. The required criteria for the existence of solutions to the suggested boundary value problems are proved based on the conditions of two versions of Krasnoselskii's fixed point theorems. Some examples are also provided to numerically demonstrate the consistency of the results.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Health Care Sciences & Services
Sonja Zehetmayer, Martin Posch, Franz Koenig
Summary: The concept of online control of the False Discovery Rate has been introduced for platform trials, and several heuristic variations of the LOND procedure have been proposed to achieve this concept.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Mathematics, Applied
Jarunee Soontharanon, Thanin Sitthiwirattham
Summary: The paper investigates the existence results of a fractional (p, q)-integrodifference equation with periodic fractional (p, q)-integral boundary conditions using Banach and Schauder's fixed point theorems. Additionally, properties of (p, q)-integral are presented as tools for calculations in the study.
Article
Biotechnology & Applied Microbiology
Xinzhou Ge, Yiling Elaine Chen, Dongyuan Song, MeiLu McDermott, Kyla Woyshner, Antigoni Manousopoulou, Ning Wang, Wei Li, Leo D. Wang, Jingyi Jessica Li
Summary: In high-throughput biological data analysis, Clipper is a general statistical framework that controls the false discovery rate without relying on p-values or specific data distributions, outperforming existing methods in a wide range of applications.
Article
Biology
Hong Zhang, Zheyang Wu
Summary: This paper introduces the GFisher method, which can handle different statistical problems flexibly, and improves the accuracy and robustness of p-value calculation through methods such as moment-ratio matching and joint-distribution surrogating.
Article
Genetics & Heredity
Xiaohui Chen, Hong Zhang, Ming Liu, Hong-Wen Deng, Zheyang Wu
Summary: This study proposes a new signal-adaptive analysis pipeline, using the oTFisher method, to combine SNP p-values in genetic association studies. The method demonstrates robust statistical power and identifies important SNPs contributing to the overall significance of the SNP set. Extensive simulations and real data analysis show that the oTFisher method outperforms traditional approaches.
FRONTIERS IN GENETICS
(2022)
Article
Computer Science, Artificial Intelligence
Ovgucan Karadag Erdemir
Summary: This study introduces a pseudo-maximization by parts method for parameter estimation of pseudo-copula regression models. It develops an iterative algorithm based on sub- and main score equations obtained from the pairwise log-likelihood function. The proposed algorithm, with the inclusion of a pseudo-Gaussian copula function, achieves better results in terms of lower errors compared to the maximization by parts algorithm, as demonstrated using real Turkish comprehensive insurance data for 2017.
Article
Engineering, Multidisciplinary
Shih-Wen Liu, Chien-Wei Wu
Summary: Acceptance sampling plans are used when sample testing is destructive or costly, ensuring reliable lot determination from a reasonable sample size. The proposed modified VRGS plan integrated with a critical-value-adjusted switching mechanism provides an adaptive assessment of varied quality levels, outperforming conventional methods in reducing average sample number and discriminatory power.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2022)
Review
Cardiac & Cardiovascular Systems
David C. McGiffin, Geoff Cumming, Paul S. Myles
Summary: This study highlights the widespread misunderstanding and misuse of p-values in cardiac surgical literature, emphasizing the need for alternative methods such as effect estimates and confidence intervals. It also stresses the unreliability and limited usefulness of p-values in decision making, recommending researchers to adopt Open Science practices and better techniques for improving research trustworthiness.
JOURNAL OF CARDIAC SURGERY
(2021)
Article
Mathematical & Computational Biology
Ajit C. Tamhane, Dong Xi, Jiangtao Gou
Summary: This paper reviews multiple group sequential Holm (GSHM) type procedures and compares their relationships and advantages. Through simulation studies, it was found that the step-up group sequential Hochberg (GSHC) procedure has better control over FWER and is more powerful.
STATISTICS IN MEDICINE
(2021)
Article
Engineering, Electrical & Electronic
Jianhua Pei, Ziyu Wang, Jingyu Wang, Dongyuan Shi
Summary: This paper proposes a robust fast PMU measurement recovery algorithm based on improved singular spectrum analysis of Hankel structures. The algorithm can recover noise-contaminated measurements and complement missing data in observed measurements corrupted by low SNRs. Numerical case studies demonstrate that the algorithm can recover measurements with higher accuracy than existing methods and satisfy the latency margins of various power system synchrophasor application scenarios.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2022)
Article
Engineering, Multidisciplinary
Fanbing Meng, Jun Yang
Summary: Product quality is important for product sale, and process capability analysis is used to assess and ensure process quality. Cpm${C}_{pm}$ index is commonly used to measure process capability, but existing testing methods have limitations under small or medium sample sizes. To address this, we propose a novel hypothesis testing method based on generalized p-value theory. Mathematical expressions are derived for normal, gamma, and Weibull distributions commonly used in engineering. Simulations show the proposed method has better performance and a new process capability analysis procedure is designed.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Mathematical & Computational Biology
Anastasios A. Tsiatis, Marie Davidian
Summary: The primary analysis in clinical trials usually involves inference on a scalar treatment effect parameter. However, in trials with a time-lagged outcome, a general group sequential framework is proposed to analyze the data, taking into account censoring and covariate information, resulting in stronger evidence for early stopping compared to standard approaches.
STATISTICS IN MEDICINE
(2022)
Article
Biology
Joshua Habiger, David Watts, Michael Anderson
Article
Statistics & Probability
Melinda H. McCann, Joshua D. Habiger
AMERICAN STATISTICIAN
(2020)
Article
Neuroimaging
Raeesa Gupte, Sarah Christian, Paul Keselman, Joshua Habiger, William M. Brooks, Janna L. Harris
BRAIN IMAGING AND BEHAVIOR
(2019)
Article
Statistics & Probability
Joshua D. Habiger
Article
Statistics & Probability
Joshua D. Habiger, Edsel A. Pena
JOURNAL OF MULTIVARIATE ANALYSIS
(2014)
Article
Statistics & Probability
Michael W. Robbins, Sujit K. Ghosh, Joshua D. Habiger
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2013)
Article
Statistics & Probability
Edsel A. Pena, Joshua D. Habiger, Wensong Wu
Article
Statistics & Probability
Joshua D. Habiger, Melinda H. McCann, Joshua M. Tebbs
STATISTICS & PROBABILITY LETTERS
(2013)
Article
Statistics & Probability
Joshua D. Habiger, Akim Adekpedjou
STATISTICS & PROBABILITY LETTERS
(2014)
Article
Agronomy
Alimamy Fornah, Michael Anderson, Joshua Habiger
Summary: The production of auxin by bacteria is negatively associated with wheat biomass productivity, particularly in culturally isolated bacteria. Fast growing colonies produce significantly higher auxin production compared to slow growing colonies.
COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
(2021)
Article
Statistics & Probability
Joshua Habiger, Ye Liang
Summary: Recent literature has shown that statistically significant results are often not replicated due to a high false positive rate (FPR) or false discovery rate (FDR). This article demonstrates the use of the local false discovery rate (lfdr) as a statistic to address the FPR and derive publication policies to control the community-wide FDR. It also highlights the limitations of relying solely on p-values for addressing the community-wide FDR.
AMERICAN STATISTICIAN
(2022)
Article
Agriculture, Dairy & Animal Science
Frank Kiyimba, Drew Cassens, Steven D. Hartson, Janet Rogers, Joshua Habiger, Gretchen G. Mafi, Ranjith Ramanathan
Summary: The objective of this study was to determine the protein and metabolite profiles associated with slightly elevated pH in darkened beef. Enzymes related to glycogen metabolism and glycolytic pathways were found to be more differentially expressed than metabolites. Higher oxygen consumption and metmyoglobin reducing activity were observed in darker steaks compared to those with normal pH. Understanding the factors contributing to dark color in beef can help reduce carcass discounts.
JOURNAL OF ANIMAL SCIENCE
(2023)
Article
Neurosciences
Rebecca J. J. Lepping, Hung-Wen Yeh, Brent C. C. McPherson, Morgan G. G. Brucks, Mohammad Sabati, Rainer T. T. Karcher, William M. M. Brooks, Joshua D. D. Habiger, Vlad B. B. Papa, Laura E. E. Martin
Summary: Both qualitative and quantitative quality control methods are crucial for ensuring high quality and accuracy in rs-fMRI data analysis.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Statistics & Probability
David D. Watts, Joshua D. Habiger
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2018)
Article
Statistics & Probability
Omidali Aghababaei Jazi
Summary: In this paper, a pseudo-partial likelihood estimation method is proposed to estimate parameters in the Cox proportional hazards model with right-censored and biased sampling data by adjusting sample risk sets. The asymptotic properties of the resulting estimator are studied, and a simulation study is conducted to illustrate the finite sample performance. The proposed method is also applied to analyze a set of HIV/AIDS data.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Liya Fu, Shuwen Hu, Jiaqi Li
Summary: Empirical likelihood (EL) is an effective nonparametric method that combines estimating equations flexibly and adaptively. A penalized EL method based on robust estimating functions is proposed for variable selection in a high-dimensional model, allowing the dimensions to grow exponentially with the sample size. The proposed method improves robustness and effectiveness in the presence of outliers or heavy-tailed data. Extensive simulation studies and a real data example demonstrate the enhanced variable selection accuracy when dealing with heavy-tailed data or outliers.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Yifan Sun, Ziyi Liu, Wu Wang
Summary: This paper extends the classical functional linear regression model to allow for heterogeneous coefficient functions among different subgroups of subjects. A penalization-based approach is proposed to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. The paper provides an effective computational algorithm and establishes the oracle properties and estimation consistency of the model. Extensive numerical simulations demonstrate its superiority compared to competing methods, and an analysis of an air quality dataset leads to interesting findings and improved predictions.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Takemi Yanagimoto, Yoichi Miyata
Summary: A Bayesian estimator is proposed to improve the conditional maximum likelihood estimation by introducing a pair of priors. The conditional maximum likelihood estimation is explained using the posterior mode under a prior, and a promising estimator is defined using the posterior mean under a corresponding prior. The advantages of this approach include two different optimality properties of the induced estimator, the ease of various extensions, and the possible treatments for a finite sample size. The existing approaches are discussed and critiqued.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Sameera Hewage, Yongli Sang
Summary: This paper introduces a new method for measuring dependence, the categorical Gini correlation rho(g), and proposes a Jackknife empirical likelihood approach for constructing confidence intervals. Simulation studies and real data applications demonstrate competitive performance of the proposed method in terms of coverage accuracy and interval length.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Isadora Antoniano-Villalobos, Cristiano Villa, Stephen G. Walker
Summary: Constructing objective priors for multidimensional parameter spaces is challenging, and a common approach assumes independence and uses standard objective methods to obtain marginal distributions. In this paper, a novel objective prior is proposed by extending the objective method for one-dimensional case, allowing for a dependence structure in multidimensional parameter spaces.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Hui Li, Liuqing Yang, Kashinath Chatterjee, Min-Qian Liu
Summary: Supersaturated design (SSD) plays a crucial role in factor screening, and E(f(NOD)) criterion is one of the most widely used criteria for evaluating multi-level and mixed-level SSDs. This paper provides methods to construct multi-level E(f(NOD)) optimal SSDs with general run sizes, which can also be extended to mixed-level SSDs. The main idea of these methods is to combine two processed generalized Hadamard matrices with the expansive replacement method. These proposed methods are easy to implement, and the non-orthogonality between any two columns of the resulting SSDs is well controlled by that of the source designs.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Victoria L. Leaver, Robert G. Clark, Pavel N. Krivitsky, Carole L. Birrell
Summary: This article compares three likelihood approaches to estimation under informative sampling and examines their efficiency and asymptotic variance. The study shows that sample likelihood estimation approaches the efficiency of full maximum likelihood estimation when the sample size tends to infinity and the sampling fraction tends to zero. However, when the sample size tends to infinity and the sampling fraction is not negligible, maximum likelihood estimation is more efficient due to considering the possibility of duplicate samples. Pseudo-likelihood estimation can perform poorly in certain cases. For a special case where the superpopulation is exponential and the selection is probability proportional to size, the anticipated variance of pseudo-likelihood estimation is infinite.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Fadoua Balabdaoui, Harald Besdziek
Summary: The two-component mixture model with known background density, unknown signal density, and unknown mixing proportion has been studied in this paper. The log-concave MLE of the signal density is computed using the estimator of Patra & Sen (2016), and its consistency and convergence are shown. The performance of this method is evaluated through a simulation study.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
V. Girardin, R. Senoussi
Summary: This paper investigates different issues related to stationarity reduction in autoregressive models, including both continuous and discrete time cases. Necessary and sufficient conditions for autoregressive models to be weakly stationary are explored, with explicit formulas for the time changes. Furthermore, the issue of stationarity reduction for discrete sequences sampled from continuous time autoregressive processes is also considered.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Juan Jose Fernandez-Duran, Maria Mercedes Gregorio-Dominguez
Summary: This paper presents the application of nonnegative trigonometric sums (NNTS) models in circular data analysis. Regression models for circular-dependent variables are constructed by fitting great circles on the parameter hypersphere, enabling the identification of different regions along the circle. The transformation of the original circular variable into a linear variable allows for the application of common linear regression methods in circular data analysis.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Miao Han, Yuanyuan Lin, Wenxin Liu, Zhanfeng Wang
Summary: The article proposes a method based on maximum rank correlation and concave fusion to automatically determine the number of subgroups, identify subgroup structure, and estimate subgroup-specific covariate effects. The method can be used without prior grouping information and is applicable to handling censored data.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Qing He, Hsin-Hsiung Huang
Summary: This article introduces a method for spatiotemporal data analysis with massive zeros, which is widely used in epidemiology and public health. The method fits zero-inflated negative binomial models using a Bayesian framework and employs latent variables from Polya-Gamma distributions to improve computational efficiency.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)