Introduction to Data Analysis and Statistical Inference

1982 ◽  
Vol 1 (3) ◽  
pp. 430
Author(s):  
Stephen M. Meyer ◽  
Carl Morris ◽  
John Rolph
Keyword(s):  
Data Analysis ◽  
Statistical Inference

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

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

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 Statistical Methods for Establishing Personalized Treatment Rules in Oncology

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junsheng Ma ◽  
Brian P. Hobbs ◽  
Francesco C. Stingo
Keyword(s):  
Data Analysis ◽  
Personalized Medicine ◽  
Statistical Inference ◽  
Statistical Methods ◽  
Treatment Strategies ◽  
Optimal Treatment ◽  
Personalized Treatment ◽  
Statistical Software ◽  
Genetic Features ◽  
Treatment Rules

The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available.

scholarly journals Analysis and Research on the Application of Sampling Big Data Statistical Method

2021 ◽  
Vol 13 ◽  
pp. 164-169
Author(s):  
Yan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Inference ◽  
Random Sampling ◽  
Time Lag ◽  
Sample Selection ◽  
Inference Problem ◽  
Probabilistic Sampling ◽  
Real Time Analysis ◽  
The Times

Under the current statistical environment and technical conditions, there is a certain lag in the publication of statistical data. This means that there is a time lag in the completion of reports, which may delay the judgment of the current economic situation. Network real-time analysis based on big data analysis has gradually become the main force of data analysis. This paper puts forward the basic idea of understanding the statistical inference problem of non-probabilistic sampling. Sampling methods can consider sample selection based on sample matching, link tracking sampling method, etc., so that the obtained non-probabilistic samples are like probabilistic samples, so the statistical inference theory of probabilistic samples can be adopted. Random sampling technology and non-random sampling technology still have many applicable scenes, which are not only the scenes of traditional sampling survey in the past, but also applied to more modern information scenes with the times.

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