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The link between multiplicative competitive interaction models and compositional data regression with a total J. Appl. Stat. (IF 1.5) Pub Date : 2024-03-16 Lukas Dargel, Christine Thomas-Agnan
This article sheds light on the relationship between compositional data (CoDa) regression models and multiplicative competitive interaction (MCI) models, which are two approaches for modeling share...
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The impact of misclassifications and outliers on imputation methods J. Appl. Stat. (IF 1.5) Pub Date : 2024-03-05 M. Templ, Markus Ulmer
Many imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized a...
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Factor model for ordinal categorical data with latent factors explained by auxiliary variables applied to the major depression inventory J. Appl. Stat. (IF 1.5) Pub Date : 2024-03-04 Alana Tavares Viana, Kelly Cristina Mota Gonçalves, Marina Silva Paez
In behavioral and social research, questionnaires are an important assessment tool, through which individuals can be categorized according to how they classify themselves in respect to a personal t...
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A robust likelihood approach to inference for paired multiple binary endpoints data J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-27 Tsung-Shan Tsou, Wei-Cheng Hsiao
We introduce a robust likelihood approach to inference for paired multiple binary endpoints data. One can easily implement the methodology without dealing with the model that incorporates a large n...
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A new factor analysis model for factors obeying a Gamma distribution J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-28 Guoqiong Zhou, Wenjiang Jiang, Shixun Lin
The traditional factor analysis model assumes that the factors obey a normal distribution, which is not appropriate in fields whose data are nonnegative. For this kind of problem, we construct a mo...
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A novel M-Lognormal–Burr regression model with varying threshold for modeling heavy-tailed claim severity data J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-26 Girish Aradhye, Deepesh Bhati, George Tzougas
In this study, we explore the potential of composite probability distributions in effectively modeling claim severity data, which encompasses a spectrum of losses, ranging from minor to substantial...
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Anticipative Bayesian classification for data streams with verification latency J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-21 Vera Hofer, Georg Krempl, Dominik Lang
Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classificatio...
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Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-22 Hirofumi Michimae, Takeshi Emura, Atsushi Miyamoto, Kazuma Kishi
In observational/field studies, competing risks and left-truncation may co-exist, yielding ‘left-truncated competing risks’ settings. Under the assumption of independent competing risks, parametric...
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Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-21 Zhujie Gu, Hae-Won Uh, Jeanine Houwing-Duistermaat, Said el Bouhaddani
In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. ...
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Multiple observers ranked set samples for shrinkage estimators J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-16 Andrew David Pearce, Armin Hatefi
Ranked set sampling (RSS) is used as a powerful data collection technique for situations where measuring the study variable requires a costly and/or tedious process while the sampling units can be ...
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Testing nonlinearity of heavy-tailed time series J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-11 Jan G. De Gooijer
A test statistic for nonlinearity of a given heavy-tailed time series process is constructed, based on the sub-sample stability of Gini-based sample autocorrelations. The finite-sample performance ...
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Vector time series modelling of turbidity in Dublin Bay J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-11 Amin Shoari Nejad, Gerard D. McCarthy, Brian Kelleher, Anthony Grey, Andrew Parnell
Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analysed for...
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Developing predictive precision medicine models by exploiting real-world data using machine learning methods J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-13 Panagiotis C. Theocharopoulos, Sotiris Bersimis, Spiros V. Georgakopoulos, Antonis Karaminas, Sotiris K. Tasoulis, Vassilis P. Plagianakos
Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which...
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Optimal Poisson subsampling decorrelated score for high-dimensional generalized linear models J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-11 Junhao Shan, Lei Wang
For high-dimensional generalized linear models (GLMs) with massive data, this paper investigates a unified optimal Poisson subsampling scheme to conduct estimation and inference for prespecified lo...
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Estimating effects of time-varying exposures on mortality risk J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-09 Trevor J. Thomson, X. Joan Hu, Bohdan Nosyk
Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization proce...
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Comparison of estimation and prediction methods for a zero-inflated geometric INAR(1) process with random coefficients J. Appl. Stat. (IF 1.5) Pub Date : 2024-02-05 R. Nasirzadeh, H. Bakouch
This study explores zero-inflated count time series models used to analyze data sets with characteristics such as overdispersion, excess zeros, and autocorrelation. Specifically, we investigate the...
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A method for optimizing text preprocessing and text classification using multiple cycles of learning with an application on shipbrokers emails J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-30 Grigorios Papageorgiou, Polychronis Economou, Sotirios Bersimis
Optimizing text preprocessing and text classification algorithms is an important, everyday task in large organizations and companies and it usually involves a labor-intensive and time-consuming eff...
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Nonparametric Shiryaev-Roberts change-point detection procedures based on modified empirical likelihood J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-23 Peiyao Wang, Wei Ning
Sequential change-point analysis, which identifies a change of probability distribution in an infinite sequence of random observations, has important applications in many fields. A good method shou...
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A Bayesian approach for evaluating equivalence over multiple groups, and comparison with frequentist tost J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-12 Jos Weusten, Ji Young Kim, Katherine Giacoletti, Jorge Vázquez, Plinio De los Santos
Manufacturing and testing of pharmaceutical products frequently occur in multiple facilities within a company’s network. It is of interest to demonstrate equivalence among the alternative testing/m...
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An improved agricultural household margin insurance scheme with insured–insurer risk protection: a time-varying copula approach J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-10 Atina Ahdika, Dedi Rosadi, Adhitya Ronnie Effendie, Gunardi
Agricultural household margin insurance (AHMI) is a crop insurance scheme that considers a farmer's trading capability. The scheme examines farmers' losses in terms of both agricultural input and o...
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Nonparametric estimation of mean residual lifetime in ranked set sampling with a concomitant variable J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-09 Ehsan Zamanzade, M. Mahdizadeh, Hani M. Samawi
The mean residual lifetime (MRL) of a unit is its expected additional lifetime provided that it has survived until time t. The MRL estimation problem has been frequently addressed in the literature...
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Propensity score matching: a tool for consumer risk modeling and portfolio underwriting J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-09 Jennifer Lewis Priestley, Eric VonDohlen
Researchers and practitioners in financial services utilize a wide range of empirical techniques to assess risk and value. In cases where known performance is used to predict future performance of ...
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Mixed-level designs with orthogonality and relatively optimal run order J. Appl. Stat. (IF 1.5) Pub Date : 2024-01-05 Wenwen Hu, Zujun Ou, Qiao Peng
Orthogonality and optimality of run order are two important and worthy to be considered criteria in design of experiment. For the mixed-level designs commonly used in the case of unequal levels of ...
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Interval-specific censoring set adjusted Kaplan–Meier estimator J. Appl. Stat. (IF 1.5) Pub Date : 2023-12-25 Yaoshi Wu, John Kolassa
We propose a non-parametric approach to reduce the overestimation of the Kaplan-Meier (KM) estimator when the event and censoring times are independent. We adjust the KM estimator based on the inte...
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Diagnostic checks in time series models based on a new correlation coefficient of residuals J. Appl. Stat. (IF 1.5) Pub Date : 2023-12-22 Jian Pei, Fukang Zhu, Qi Li
For checking time series models, the Ljung–Box, Li–Mak and Zhu–Wang statistics play an important role, which use the Pearson's correlation coefficient to implement (squared) residual (partial) auto...
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Confidence intervals and prediction intervals for two-parameter negative binomial distributions J. Appl. Stat. (IF 1.5) Pub Date : 2023-12-21 Md Mahadi Hasan, K. Krishnamoorthy
Problems of finding confidence intervals (CIs) and prediction intervals (PIs) for two-parameter negative binomial distributions are considered. Simple CIs for the mean of a two-parameter negative b...
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A note on the unimodality and log-concavity of the exponentiated Teissier distribution J. Appl. Stat. (IF 1.5) Pub Date : 2023-12-21 V. Kumaran, Vishwa Prakash Jha
Recently, Sharma et al. (Exponentiated Teissier distribution with increasing, decreasing and bathtub hazard functions, J. Appl. Stat. 49 (2022), pp. 371–393) introduced the exponentiated Teissier d...
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Discussion comments on “Exponentiated Teissier distribution with increasing, decreasing and bathtub hazard functions” J. Appl. Stat. (IF 1.5) Pub Date : 2023-12-21 Vikas Kumar Sharma, Sudhanshu V. Singh, Komal Shekhawat
In this note, we present some discussion comments on a note entitled ‘A note on the unimodality and log-concavity of the exponentiated Teissier distribution’ submitted in J. Appl. Stat. by some aut...
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Post-shrinkage strategies in statistical and machine learning for high dimensional data J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-24
Published in Journal of Applied Statistics (Ahead of Print, 2023)
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A characteristic function based circular distribution family and its goodness of fit : The flexible wrapped Linnik family J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-21 Ashis SenGupta, Moumita Roy
In this article, the primary aim is to introduce a new flexible family of circular distributions, namely the wrapped Linnik family which possesses the flexibility to model the inflection points and...
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Determination of the number of clusters through logistic regression analysis J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-20 Soumita Modak
We advise a novel measure to determine the unknown number of clusters underlying a designated sample through implementation of the parametric logistic regression model. The regression analysis is c...
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Estimating intracluster correlation for ordinal data J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-17 Benjamin W. Langworthy, Zhaoxun Hou, Gary C. Curhan, Sharon G. Curhan, Molin Wang
In this paper, we consider the estimation of intracluster correlation for ordinal data. We focus on pure-tone audiometry hearing threshold data, where thresholds are measured in 5 decibel increment...
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Comparison of two statistical methodologies for a binary classification problem of two-dimensional images J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-15 Deniz A. Sanchez S., Rubén D. Guevara G., Sergio A. Calderón V.
The present work intends to compare two statistical classification methods using images as covariates and under the comparison criterion of the ROC curve. The first implemented procedure is based o...
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Forecasting of the true satellite carbon monoxide data with ensemble empirical mode decomposition, singular value decomposition and moving average J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-14 Sameer Poongadan, M. C. Lineesh
The forecasting of carbon monoxide in the atmosphere is essential as it causes the pollution of the atmosphere and hence severe health problems for humans. This study proposes a time-series prognos...
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GWR-assisted integrated estimator of finite population total under two-phase sampling: a model-assisted approach J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-14 Nobin Chandra Paul, Anil Rai, Tauqueer Ahmad, Ankur Biswas, Prachi Misra Sahoo
In survey sampling, auxiliary information is used to precisely estimate the finite population parameters. There are several approaches available in the literature that provide a practical method fo...
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Phase II control charts for monitoring the depth-ratio of ball-bearings involving three normal variables J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-08 Li Jin, Amitava Mukherjee, Zhi Song, Jiujun Zhang
This paper investigates the problem of monitoring the ratio involving three variables, jointly distributed as trivariate normal. The Shewhart-type and two exponentially weighted moving average (EWM...
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A computationally efficient sequential regression imputation algorithm for multilevel data J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-06 Tugba Akkaya Hocagil, Recai M. Yucel
Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the mi...
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Smoothing level selection for density estimators based on the moments J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-07 Rosa M. García-Fernández, Federico Palacios-González
This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the...
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An exact projection pursuit-based algorithm for multivariate two-sample nonparametric testing applicable to retrospective and group sequential studies J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-06 Li Zou, Gregory Gurevich, Ablert Vexler
Nonparametric tests for equality of multivariate distributions are frequently desired in research. It is commonly required that test-procedures based on relatively small samples of vectors accurate...
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Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data J. Appl. Stat. (IF 1.5) Pub Date : 2023-11-03 Liugen Xue, Junshan Xie, Xiaohui Yang
In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed t...
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A longitudinal study of the influence of air pollutants on children: a robust multivariate approach J. Appl. Stat. (IF 1.5) Pub Date : 2023-10-30 Ian Meneghel Danilevicz, Pascal Bondon, Valdério Anselmo Reisen, Faradiba Sarquis Serpa
This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and te...
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Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets J. Appl. Stat. (IF 1.5) Pub Date : 2023-10-30 Matthieu Garcin, Jules Klein, Sana Laaribi
The time-varying kernel density estimation relies on two free parameters: the bandwidth and the discount factor. We propose to select these parameters so as to minimize a criterion consistent with ...
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Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials J. Appl. Stat. (IF 1.5) Pub Date : 2023-10-27 Nikola Surjanovic, Thomas M. Loughin
The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from s...
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Editorial to the special issue: modern streaming data analytics J. Appl. Stat. (IF 1.5) Pub Date : 2023-10-05 Yajun Mei, Jay Bartroff, Jie Chen, Georgios Fellouris, Ruizhi Zhang
Published in Journal of Applied Statistics (Vol. 50, No. 14, 2023)
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The zero-and-plus/minus-one inflated extended-Poisson distribution J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-27 Maher Kachour, Christophe Chesneau
In this paper, we introduce a new distribution defined on Z, called the ZPMOIEP distribution, which can be viewed as a natural extension of the zero-and-one-inflated Poisson (ZOIP) distribution. It...
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Bayesian transformation model for spatial partly interval-censored data J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-27 Mingyue Qiu, Tao Hu
The transformation model with partly interval-censored data offers a highly flexible modeling framework that can simultaneously support multiple common survival models and a wide variety of censore...
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Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-24 Sedigheh Omidvar, Mohammad Jafari Jozani, Nader Nematollahi, Wiliam D. Leslie
Osteoporosis is a metabolic bone disorder that is characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture. Osteoporosis is highly prevalent among women over ...
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Estimating linear mixed effect models with non-normal random effects through saddlepoint approximation and its application in retail pricing analytics J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-24 Hao Chen, Lanshan Han, Alvin Lim
Linear Mixed Effects (LME) models are powerful statistical tools that have been employed in many different real-world applications such as retail data analytics, marketing measurement, and medical ...
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The spike-and-slab lasso and scalable algorithm to accommodate multinomial outcomes in variable selection problems J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-14 Justin M. Leach, Nengjun Yi, Inmaculada Aban, The Alzheimer's Disease Neuroimaging Initiative
Spike-and-slab prior distributions are used to impose variable selection in Bayesian regression-style problems with many possible predictors. These priors are a mixture of two zero-centered distributions with differing variances, resulting in different shrinkage levels on parameter estimates based on whether they are relevant to the outcome. The spike-and-slab lasso assigns mixtures of double exponential
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Point estimation and related classification problems for several Lindley populations with application using COVID-19 data J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-06 Debasmita Bal, Manas Ranjan Tripathy, Somesh Kumar
The problems of point estimation and classification under the assumption that the training data follow a Lindley distribution are considered. Bayes estimators are derived for the parameter of the Lindley distribution applying the Markov chain Monte Carlo (MCMC), and Tierney and Kadane's [Tierney and Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc
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GARTFIMA process and its empirical spectral density based estimation J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-04 Niharika Bhootna, Arun Kumar
In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed
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Prediction and model evaluation for space–time data J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-03 G. L. Watson, C. E. Reid, M. Jerrett, D. Telesca
Evaluation metrics for prediction error, model selection and model averaging on space–time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space–time prediction problems. Motivated by air pollution data collected during California wildfires
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Statistical clustering of documents via stochastic blockmodels J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-01 Paul H. Atandoh, Kevin H. Lee
As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset. But in our work, we propose a method that utilizes both text analysis method such
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A novel two-way functional linear model with applications in human mortality data analysis J. Appl. Stat. (IF 1.5) Pub Date : 2023-09-01 Xingyu Yan, Jiaqian Yu, Weiyong Ding, Hao Wang, Peng Zhao
Recently, two-way or longitudinal functional data analysis has attracted much attention in many fields. However, little is known on how to appropriately characterize the association between two-way functional predictor and scalar response. Motivated by a mortality study, in this paper, we propose a novel two-way functional linear model, where the response is a scalar and functional predictor is two-way
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Spatial analysis for interval-valued data J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-29 Austin Workman, Joon Jin Song
Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide geostatistical methods for spatial prediction, predictive performance measure for prediction assessment
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Goodness-of-fit test for the one-sided Lévy distribution J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-27 Aditi Kumari, Deepesh Bhati
The main aim of this work is to develop a new goodness-of-fit test for the one-sided Lévy distribution. The proposed test is based on the scale-ratio approach in which two estimators of the scale parameter of one-sided Lévy distribution are confronted. The asymptotic distribution of the test statistic is obtained under null hypotheses. The performance of the test is demonstrated using simulated observations
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Classifying contaminated cell cultures using time series features J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-22 Laura L. Tupper, Charles R. Keese, David S. Matteson
We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve
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Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19 J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-18 Özge Kuran, Seçil Yalaz
In this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation
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The cluster D-trace loss for differential network analysis J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-14 Han Yan, Shuhan Lu, Sanguo Zhang
A growing literature suggests that gene expression can be greatly altered in disease conditions, and identifying those changes will improve the understanding of complex diseases such as cancers or diabetes. A prevailing direction in the analysis of gene expression studies the changes in gene pathways which include sets of related genes. Therefore, introducing structured exploration to differential
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Multiple comparisons of treatment against control under unequal variances using parametric bootstrap J. Appl. Stat. (IF 1.5) Pub Date : 2023-08-09 Sarah Alver, Guoyi Zhang
ABSTRACT In one-way analysis of variance models, performing simultaneous multiple comparisons of treatment groups with a control group may be of interest. Dunnett's test is used to test such differences and assumes equal variances of the response variable for each group. This assumption is not always met even after transformation. A parametric bootstrap (PB) method is developed here for comparing multiple