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Speaking Stata: Rowwise AgEcon
Cox, Nicholas J..
Stata’s main data model treats observations in rows and variables in columns quite differently, but rowwise problems also arise that require working against the grain. This column shows how to exploit existing functions and egen functions when they exist and apply to such problems. It offers advice on how to build your own loops, egen functions, or programs when needed. Mata provides especially convenient tools for constructing many such functions and programs, centered on putting selected data into matrices and then processing each observation as a separate vector. Two programs, rowsort and rowranks, are formally published with this column.
Tipo: Article Palavras-chave: Rowsort; Rowranks; Rows; Functions; Loops; Egen; Mata; Minimum; Maximum; Median; Any; All; Distinct values; Sorting; Ranking; Research Methods/ Statistical Methods.
Ano: 2009 URL: http://purl.umn.edu/122706
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Confidence intervals for rank statistics: Percentile slopes, differences, and ratios AgEcon
Newson, Roger.
I present a program, censlope, for calculating confidence intervals for generalized Theil–Sen median (and other percentile) slopes (and per-unit ratios) of Y with respect to X. The confidence intervals are robust to the possibility that the conditional population distributions of Y , given different values of X, differ in ways other than location, such as having unequal variances. censlope uses the program somersd and is part of the somersd package. censlope can therefore estimate confounder-adjusted percentile slopes, limited to comparisons within strata defined by values of confounders, or by values of a propensity score representing multiple confounders. Iterative numerical methods have been implemented in the Mata language, enabling efficient...
Tipo: Article Palavras-chave: Somersd; Censlope; ALSPAC; Robust; Confidence interval; Rank; Nonparametric; Median; Percentile; Slope; Difference; Ratio; Kendall's τ; Somers' D; Theil–Sen; Hodges–Lehmann; Confounder adjusted; Propensity score; Research Methods/ Statistical Methods.
Ano: 2006 URL: http://purl.umn.edu/119241
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