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                  <text>VOLUME 101 Tahun 2022</text>
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                  <text>Fajar bagus W</text>
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                <text>lpdensity: Local Polynomial Density Estimation and Inference</text>
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                <text>: kernel-based nonparametrics, local polynomial, density estimation, bandwidth selection, bias correction, robust inference, boundary carpentry, R, Stata</text>
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                <text>Density estimation and inference methods are widely used in empirical work. When&#13;
the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known&#13;
boundary bias. Alternative smoothing methods are available to handle boundary points&#13;
in density estimation, but they all require additional tuning parameter choices or other&#13;
typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel&#13;
local polynomial density estimator proposed and studied in Cattaneo, Jansson, and Ma&#13;
(2020, 2022), which is boundary adaptive and involves only one tuning parameter. The&#13;
methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives. In addition to point estimation and graphical&#13;
procedures, the package offers consistent variance estimators, mean squared error optimal&#13;
bandwidth selection, robust bias-corrected inference, and confidence bands construction,&#13;
among other features. A comparison with other density estimation packages available in&#13;
R using a Monte Carlo experiment is provided.</text>
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                <text>Matias D. Cattaneo</text>
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                <text>https://www.jstatsoft.org/article/view/v101i02</text>
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                <text>Princeton University</text>
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                <text>January 2022</text>
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                <text>Fajar bagus W</text>
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                  <text>VOLUME 101 Tahun 2022</text>
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                <text>The poolr Package for Combining Independent and Dependent p Values</text>
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                <text>combining p values, dependent p values, R</text>
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                <text>The poolr package provides an implementation of a variety of methods for pooling&#13;
(i.e., combining) p values, including Fisher’s method, Stouffer’s method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett’s method. More&#13;
importantly, the methods can be adjusted to account for dependence among the tests&#13;
from which the p values have been derived assuming multivariate normality among the&#13;
test statistics. All methods can be adjusted based on an estimate of the effective number&#13;
of tests or by using an empirically-derived null distribution based on pseudo replicates that&#13;
mimics a proper permutation test. For the Fisher, Stouffer, and inverse chi-square methods, the test statistics can also be directly generalized to account for dependence, leading&#13;
to Brown’s method, Strube’s method, and the generalized inverse chi-square method. In&#13;
this paper, we describe the various methods, discuss their implementation in the package,&#13;
illustrate their use based on several examples, and compare the poolr package with several&#13;
other packages that can be used to combine p value</text>
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                <text>Ozan Cinar</text>
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            <description>A related resource from which the described resource is derived</description>
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                <text>https://www.jstatsoft.org/article/view/v101i01</text>
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              <elementText elementTextId="88219">
                <text>Maastricht University</text>
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                <text>January 2022</text>
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                <text>Fajar bagus W</text>
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                <text>English</text>
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