21 papers · ranked by Valyu relevance
Dhrubajyoti Ghosh, Samhita Pal
Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and…
Chenchen Ma, Jimmy de la Torre, Gongjun Xu
A number of parametric and nonparametric methods for estimating cognitive diagnosis models (CDMs) have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. In this…
Arturo Garrocho-Rangel, Saray Aranda-Romo, Rita Martínez-Martínez, Verónica Zavala-Alonso + 4 more
This article provides the foundation for employing nonparametric testing in dental clinical research. To make wise judgments in their research, investigators should learn more about the main nonparametric tests and their particular uses. Biostatistical analysis is essential in dental research; dental research…
Alfred K. Adzika, Prudence Djagba
This thesis aims to invent new approaches for making inferences with the k-means algorithm. k-means is an iterative clustering algorithm which starts by randomly assigning k centroids, then assigns data points to the nearest centroid, and updates centroids based on the mean of assigned points. This process continues…
Mustain Ramli, I Nyoman Budiantara, Vita Ratnasari
Nonparametric regression is an approximation method in regression analysis that is not constrained by the assumption of knowing the regression curve. One of the functions to approximate the curve is a Fourier series function. The nonparametric regression model with approximation of a Fourier series function has been…
Richard A. Chechile, Daniel H. Barch Jr.
Nonparametric (or distribution-free) statistics have been widely used in psychological research because behavioral data can be messy and inconsistent with the Gaussian model for measurement error. Distribution-free procedures only use categorical or rank information, so they avoid the problems of outliers and…
Julián Urbano
In benchmarking of Information Retrieval systems, the Wilcoxon signed-rank test is often treated as a safer alternative to the t-test. This belief is fueled by textbooks and recommendations that portray Wilcoxon as the proper non-parametric alternative because metric scores are not normally distributed. We argue that…
Michael Malek-Ahmadi, Alexandra M. Reed, Dylan X. Guan
While the most common statistical tests assume that the error of the dependent variable follows a normal distribution, dependent variables in translational neuroscience studies often fail to meet this assumption. Common statistical tests like the t test and ANOVA are based on the normality assumption, but quite often…
Jiahui Zhang, Yuqing Yuan, Ziying Qiu, Feng Li + 1 more
Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT), in conjunction with nonparametric methodologies, is an adaptive assessment tool utilized for diagnosing students’ knowledge mastery within smaller educational contexts. Expanding upon this framework, this study introduces the blocked procedure previously used…
Kazuhiro Yamaguchi, Yanlong Liu, Gongjun Xu
This study extends the loss function-based parameter estimation method for diagnostic classification models proposed by Ma, de la Torre, et al. (2023, Psychometrika) to consider prior knowledge and uncertainty of sampling. To this end, we integrate the loss function-based estimation method with the generalized Bayesian…
Calvin Guan, Ashis Gangopadhyay
Although there are many methods available in the literature to compare the covariance structures of two populations, few are suitable for clinical application due to the inability to account for covariate(s) that affect the dependence structure of the variables being investigated. A common method is to adjust the…
Kexuan Li, Lingli Yang, Shaofei Zhao, Susie Sinks + 2 more
'Peng Sun'] Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other…
Authors not listed
Learning aqueous solubility remains a key challenge in drug development for improving oral bioavailability. Traditional data-driven solubility estimations using standard supervised models, however, can often suppress the information embedded in a molecule’s chemical properties and the intricate connectivity of its…
Andrew P. Woodward
Non-compartmental analysis (NCA) is a popular strategy for obtaining estimates of pharmacokinetic parameters, while requiring both minimal structural assumptions, and limited input by the analyst. As typically applied, its scope and depth are constrained by its statistical simplicity. Embedding the NCA within a…
Mengyu He, Ni Zhao, Glen A. Satten
Advances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed…
Miha Moškon
Analysis of rhythmic data has become an important aspect in biological and medical science, as well as other fields of science. Parametric methods based on trigonometric regression reflect several advantages in comparison to their alternatives. Software packages for parametric analysis of rhythmic data are mostly based…
Łukasz Chrostowski, Piotr Chlebicki, Maciej Beręsewicz
The following paper presents nonprobsvy – an R package for inference based on nonprobability samples. The package implements various approaches that can be categorized into three groups: prediction-based approach, inverse probability weighting and doubly robust approach. In the package, we assume the existence of…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Alberto Arletti, Maria Letizia Tanturri, Omar Paccagnella
Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels and even comments scraped from the internet can offer valuable insights into political preferences. However, such data is often affected by significant selection bias, as online…
Aaditya Prasad Gupta
Biological systems, at all scales of organization from nucleic acids to ecosystems, are inherently complex and variable. Therefore mathematical models have become an essential tool in systems biology, linking the behavior of a system to the interaction between its components. Parameters in empirical mathematical models…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…