Quantified Neurophysiology with mapping: Statistical inference, Exploratory and Confirmatory data analysis

1990 ◽  
Vol 3 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Frank H. Duffy ◽  
Kenneth Jones ◽  
Peter Bartels ◽  
Marilyn Albert ◽  
Gloria B. McAnulty ◽  
...  
Keyword(s):  
Data Analysis ◽  
Statistical Inference ◽  
Confirmatory Data Analysis

Descriptive Data Analysis: A Concept between Confirmatory and Exploratory Data Analysis

1987 ◽  
Vol 26 (02) ◽  
pp. 77-88 ◽  
Author(s):  
K. Abt
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Analytical Models ◽  
Controlled Study ◽  
Complex Data ◽  
Descriptive Data ◽  
Relevant Effect ◽  
Significance Levels ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

SummaryConfirmatory Data Analysis (CDA) in randomized comparative (“controlled”) studies with many variables and/or time points of interest finds its limitations in the multiplicity of desired inferential statements which leads to unfeasibly small adjusted significance levels (“Bon-ferronization”) and, thereby, to unduly increased risks of not rejecting false hypotheses. In general, analytical models adequate for such complex data structures and suitable for practical use do not exist as yet. Exploratory Data Analysis (EDA), on the other hand, is usually intended to generate hypotheses and not to lead to final conclusions based on the results of the study.In this paper, it is proposed to fill the conceptual gap between CDA and EDA by “Descriptive Data Analysis” (“DDA”) which concept is mainly based on descriptive inferential statements. The results of a DDA in a controlled study are interpreted simultaneously on the basis of the investigator’s experience with respect to numerically relevant treatment effect differences and on “descriptive significances” as they appear in “near regular” patterns corresponding to the resulting relevant effect differences. A DDA may also contain confirmatory parts and/or tests on global hypotheses at a prechosen maximum risk α of erroneously rejecting true hypotheses. The paper is in parts expository and is addressed to investigators as well as statisticians.

Introduction to Data Analysis and Statistical Inference.

1984 ◽  
Vol 79 (385) ◽  
pp. 242
Author(s):  
William F. Taylor ◽  
Carl N. Morris ◽  
John E. Rolph
Keyword(s):  
Data Analysis ◽  
Statistical Inference

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation (Preprint)

2020 ◽  
Author(s):  
Alfonso J. Rodriguez-Morales ◽  
Ram Kumar Singh ◽  
S.S. Singh ◽  
A. K. Pandey ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Arima Model ◽  
Time Estimation ◽  
The United States ◽  
World Health ◽  
Disease Spread ◽  
Analysis Model ◽  
Indian States ◽  
The World ◽  
Confirmatory Data Analysis

BACKGROUND The highly contagious Coronavirus disease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). It is identified as the most severe pandemic disease in human history during the past 100 years. Due to non-availability of specific medication, the World Health Organization (WHO) suggested various measures of precautions and social distance in between the people for the restricting the spread of the COVID-19 disease. Various nation’s administration including the India government called for the regional and local lockdown. OBJECTIVE We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. METHODS The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. RESULTS The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. CONCLUSIONS The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. CLINICALTRIAL wqew ere re

Model-free data condensation

1991 ◽  
Vol 335 (1636) ◽  
pp. 1-50
Keyword(s):  
Data Analysis ◽  
Statistical Inference ◽  
Model Free ◽  
Classical Statistics ◽  
Free Data

One aim of data analysis is its condensation, namely capturing its gist in an apposite way. This paper addresses the problem of constructing and assessing such condensations without reference to mechanisms which might have generated the data. The results obtained lead to non-probabilistic interpretations of some well-known inferential procedures of classical statistics and thereby shed new light on the structure of statistical inference and the theory of probability.

Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis

2004 ◽  
Vol 12 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Stephen M. Shellman
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Statistical Inference ◽  
Science Studies ◽  
Parameter Estimates ◽  
Temporal Aggregation ◽  
Event Data ◽  
Political Analysis ◽  
The Impact ◽  
Event Data Analysis

While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

Heavy tailed capital incomes: Zenga index, statistical inference, and ECHP data analysis

Extremes ◽  
2013 ◽  
Vol 17 (1) ◽  
pp. 127-155 ◽  
Author(s):  
Francesca Greselin ◽  
Leo Pasquazzi ◽  
Ričardas Zitikis
Keyword(s):  
Data Analysis ◽  
Statistical Inference ◽  
Heavy Tailed

scholarly journals Statistical Inferences and Analysis based on the COVID-19 data from the United States

2021 ◽  
Author(s):  
Nivedita Rethnakar
Keyword(s):  
United States ◽  
Data Analysis ◽  
Statistical Inference ◽  
Respiratory Diseases ◽  
Temporal Dynamics ◽  
The United States ◽  
Mortality Statistics ◽  
Statistical Inferences ◽  
Race Ethnicity ◽  
The Impact

AbstractThis paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo-graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with sufficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy preparation and thus adequately preparing, should a similar situation arise in the future.

scholarly journals Current exploratory and confirmatory issues in data analysis in psychology

2010 ◽  
Vol 3 (1) ◽  
pp. 4-8
Author(s):  
Fernando Marmolejo-Ramos
Keyword(s):  
Data Analysis ◽  
San Francisco ◽  
Exploratory Data Analysis ◽  
American Psychological Association ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
American Psychological ◽  
Exploratory Data ◽  
Confirmatory Data Analysis ◽  
Analytical Tools

In 1968 John Tukey gave a speech at the American Psychological Association in San Francisco about the relevance of proper data analysis in Psychology (Tukey, 1969). His closing message was that “data analysis needs to be both exploratory and confirmatory” (p. 90). Exploratory data analysis (or EDA) is an approach to analysing data in order to formulate sound hypotheses, whereas confirmatory data analysis (CDA) is a method to test those hypotheses (a.k.a., statistical hypothesis testing). As Tukey announced in his speech, these two analytical tools have been, and are somewhat still, at odds. This special issue presents sixteen papers that cover relevant topics in EDA and CDA with the purpose of bringing together seemingly disparate issues.

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