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Univariate Singular Spectrum Analysis
2018A concise description of univariate Singular Spectrum Analysis (SSA) is presented in this chapter. A step-by-step guide for performing filtering, forecasting as well as forecasting interval using univariate SSA and associated R codes is also provided. After reading this chapter, the reader will be able to select two basic, but very important, choices ...
Hossein Hassani, Rahim Mahmoudvand
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Multiparameter Univariate Bayesian Analysis
Journal of the American Statistical Association, 1979Abstract Bayesian analysis using Monte Carlo integration is a powerful method for univariate inference. This approach makes possible multiparameter flexibility within families of univariate distributions. These distributions are defined in this article by increasing spline functions superimposed on probability paper coordinate systems.
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2013
Let us return to our students from the previous chapter. After completing their survey of bread spreads, they have now coded the data from the 850 respondents and entered them into a computer. In the first step of data assessment, they investigate each variable – for example, average respondent age – separately. This is called univariate analysis.
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Let us return to our students from the previous chapter. After completing their survey of bread spreads, they have now coded the data from the 850 respondents and entered them into a computer. In the first step of data assessment, they investigate each variable – for example, average respondent age – separately. This is called univariate analysis.
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1991
As outlined in Chapter 1 the techniques of statistical inference usually require that assumptions be made regarding the sample data. Such assumptions usually include the type of sampling process that produced the data and in some cases the nature of the population distribution from which the sample was drawn.
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As outlined in Chapter 1 the techniques of statistical inference usually require that assumptions be made regarding the sample data. Such assumptions usually include the type of sampling process that produced the data and in some cases the nature of the population distribution from which the sample was drawn.
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Descriptive Statistics 1: Univariate Analysis
2021Mastering descriptive statistics is mandatory for a geologist. Chapter 5 shows how to describe a geological data set using Python programming, starting with basic metrics such as the location, dispersion, and degree of symmetry of a univariate data set. It then shows how to perform descriptive statistics in pandas and introduces box plot diagrams.
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Univariate Extreme Value Analysis
2016Abstract This chapter reviews the basics of univariate extreme value analysis. The generalized extreme value (GEV) distribution is introduced as the limit distribution of sample maxima with appropriate standardization. The domains of attraction are reviewed and illustrated numerically with simulation for distributions that have different tail behaviors.
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Univariate Screening Measures for Cluster Analysis
Multivariate Behavioral Research, 1995lnciusion of irrelevant variables in a cluster analysis adversely affects subgroup recovery. This article examines using moment-based statistics to screen variables; only variables which pass the screening are then used in clustering. Normal mixtures are analytically shown often to possess negative kurtosis.
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Univariate Time-Series Analysis
2007Abstract The Roll model described in the last chapter is a simple structural model, with a clear mapping to parameters (the variance and autocovariance of price changes) that are easily estimated. There are many interesting questions, though, that go beyond parameter estimation. We might want to forecast prices beyond the end of our data
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