Results 61 to 70 of about 419,428 (264)
A five-decision testing procedure to infer on unidimensional parameter [PDF]
arXiv, 2017 A statistical test can be seen as a procedure to produce a decision based on observed data, where some decisions consist of rejecting a hypothesis (yielding a significant result) and some do not, and where one controls the probability to make a wrong rejection at some pre-specified significance level.
arxiv
Hypothesis testing under uniform-block covariance structures [PDF]
arXiv, 2023 A block covariance structure is widely observed across large-scale and high-dimensional datasets in diverse fields such as biology, medicine, engineering, economics, and finance. This pattern entails partitioning a covariance matrix into uniform blocks, where each block exhibits equal variances and covariances.
arxiv
Manipulating the Alpha Level Cannot Cure Significance Testing
Frontiers in Psychology, 2018 We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science.
David Trafimow +67 more
doaj +1 more source
Molecular Oncology, EarlyView.This study explores how sepsis affects GC progression by creating an immunosuppressive environment. Our findings reveal that sepsis promotes immune dysregulation, enhancing tumor growth and metastasis. Targeting the PD‐1/PD‐L1 pathway with monoclonal antibodies shows potential for restoring immune function and improving outcomes in cancer patients ...
Yiding Wang +10 more
wiley +1 more source
Data-driven goodness-of-fit tests [PDF]
arXiv, 2007 We propose and study a general method for construction of consistent statistical tests on the basis of possibly indirect, corrupted, or partially available observations. The class of tests devised in the paper contains Neyman's smooth tests, data-driven score tests, and some types of multi-sample tests as basic examples.
arxiv
Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference [PDF]
Machine Learning and Knowledge Extraction, 2019 Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their ...
Frank Emmert-Streib, Matthias Dehmer
openaire +4 more sources
Patterns, 2020 Summary: Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypothesis testing and binary classification.
Jingyi Jessica Li, Xin Tong
doaj
Hypothesis testing, type I and type II errors
Industrial Psychiatry Journal, 2009 Hypothesis testing is an important activity of empirical research and evidence-based medicine. A well worked up hypothesis is half the answer to the research question.
Amitav Banerjee +4 more
doaj +1 more source
Molecular Oncology, EarlyView.Tumor microenvironment drives cancer formation and progression. We analyzed the role of human cancer‐associated adipocytes from patients with renal cell carcinoma (RCC) stratified as lean, overweight, or obese. RNA‐seq demonstrated that, among the most altered genes involved in the tumor–stroma crosstalk, are ADAM12 and CYP1B1, which were proven to be ...
Sepehr Torabinejad +13 more
wiley +1 more source
Nonparametric Hypothesis Tests for Statistical Dependency [PDF]
IEEE Transactions on Signal Processing, 2004 Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multitarget tracking and computer vision. In this paper, we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations
Alan S. Willsky +2 more
openaire +2 more sources