stephenberg.github.io/index.xml at master · stephenberg/stephenberg.github.io · GitHub

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  <channel>
    <title>Home on Stephen Berg</title>
    <link>https://stephenberg.github.io/</link>
    <description>Recent content in Home on Stephen Berg</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <lastBuildDate>Tue, 01 Dec 2020 21:13:14 -0500</lastBuildDate><atom:link href="https://stephenberg.github.io/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Hello R Markdown</title>
      <link>https://stephenberg.github.io/post/2020/12/01/hello-r-markdown/</link>
      <pubDate>Tue, 01 Dec 2020 21:13:14 -0500</pubDate>

      <guid>https://stephenberg.github.io/post/2020/12/01/hello-r-markdown/</guid>
      <description>R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
You can embed an R code chunk like this:
summary(cars)## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.</description>
    </item>

    <item>
      <title>Another Note on A blogdown Tutorial</title>
      <link>https://stephenberg.github.io/note/2017/06/14/another-note/</link>
      <pubDate>Wed, 14 Jun 2017 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/note/2017/06/14/another-note/</guid>
      <description>I just discovered an awesome tutorial on blogdown written by Alison. I have to admit this is the best blogdown tutorial I have seen so far.</description>
    </item>

    <item>
      <title>A Quick Note on Two Beautiful Websites</title>
      <link>https://stephenberg.github.io/note/2017/06/13/a-quick-note/</link>
      <pubDate>Tue, 13 Jun 2017 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/note/2017/06/13/a-quick-note/</guid>
      <description>To me, the two most impressive websites based on blogdown are:
Rob J Hyndman&amp;rsquo;s personal website. Live Free or Dichotomize by Lucy and Nick et al. I&amp;rsquo;m sure there will be more.</description>
    </item>

    <item>
      <title>A Plain Markdown Post</title>
      <link>https://stephenberg.github.io/post/2016/02/14/a-plain-markdown-post/</link>
      <pubDate>Sun, 14 Feb 2016 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/post/2016/02/14/a-plain-markdown-post/</guid>
      <description>This sample post is mainly for blogdown users. If you do not use blogdown, you can skip the first section.
1. Markdown or R Markdown This is a post written in plain Markdown (*.md) instead of R Markdown (*.Rmd). The major differences are:
You cannot run any R code in a plain Markdown document, whereas in an R Markdown document, you can embed R code chunks (```{r}); A plain Markdown post is rendered through Blackfriday, and an R Markdown document is compiled by rmarkdown and Pandoc.</description>
    </item>

    <item>
      <title>Lorem Ipsum</title>
      <link>https://stephenberg.github.io/post/2015/07/23/lorem-ipsum/</link>
      <pubDate>Thu, 23 Jul 2015 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/post/2015/07/23/lorem-ipsum/</guid>
      <description>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Quisque mattis volutpat lorem vitae feugiat.</description>
    </item>

    <item>
      <title>About</title>
      <link>https://stephenberg.github.io/about/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/about/</guid>
      <description>XMin is the first Hugo theme I have designed. The original reason that I wrote it was I needed a minimal example of Hugo themes when I was writing the blogdown book. Basically I wanted a simple theme that supports a navigation menu, a home page, other single pages, lists of pages, blog posts, categories, tags, and RSS. That is all. Nothing fancy. In terms of CSS and JavaScript, I really want to keep them minimal.</description>
    </item>

    <item>
      <title>Contact</title>
      <link>https://stephenberg.github.io/contact/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/contact/</guid>
      <description>Email: sqb6128@psu.edu Github Penn State faculty page</description>
    </item>

    <item>
      <title>Research</title>
      <link>https://stephenberg.github.io/research/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/research/</guid>
      <description>Manuscripts Song, H., &amp;amp; Berg, S. (2024) Weighted shape-constrained estimation for the autocovariance sequence from a reversible Markov chain. [arXiv]
Song, H., &amp;amp; Berg, S. (2024, in press). Multivariate moment least-squares variance estimators for reversible Markov chains. Journal of Computational and Graphical Statistics [journal] [arXiv]
Berg, S., &amp;amp; Song, H. (2023). Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain. The Annals of Statistics, vol. 51, pp. 2440-2470 [journal] [arXiv]</description>
    </item>

    <item>
      <title>Software</title>
      <link>https://stephenberg.github.io/software/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/software/</guid>
      <description>I have contributed to three publically available statistical software packages, and I hope to add more in the future.
momentLS Autocovariance estimation for reversible Markov chains. The package implements the estimators in “Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain” ( https://arxiv.org/abs/2207.12705 ). I am a contributor to this package, with Hyebin Song.
GitHub
automultinomial For regression models (similar to logistic regression) of spatially correlated discrete data.</description>
    </item>

    <item>
      <title>Talks</title>
      <link>https://stephenberg.github.io/talks/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/talks/</guid>
      <description>Upcoming:
Past:
&amp;ndash; JSM 2024 &amp;ldquo;Statistical and computational aspects of shape-constrained inference for covariance function estimation&amp;rdquo; slides
&amp;ndash; EAC ISBA 2024 (virtual) “Shape-constrained inference for covariance function estimation” (June 26, 2024) slides
&amp;ndash; CMStatistics 2023 &amp;ldquo;Statistical and computational aspects of shape-constrained inference for covariance function estimation&amp;rdquo; (December 18, 2023) slides
&amp;ndash; PSU Statistics Graduate Student Association workshop talk (October 12, 2023) slides
&amp;ndash; SMAC seminar, Penn State Department of Statistics, &amp;ldquo;Statistical and computational aspects of shape-constrained inference for covariance function estimation&amp;rdquo; (September 8, 2023)</description>
    </item>

    <item>
      <title>Teaching</title>
      <link>https://stephenberg.github.io/teaching/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

      <guid>https://stephenberg.github.io/teaching/</guid>
      <description>Penn State: Spring 2025: &amp;ndash; Statistics 540 (Statistical Computing)
Fall 2024: &amp;ndash;Statistics 414 (Introduction to Probability Theory)
Spring 2024: &amp;ndash; Statistics 414 (Introduction to Probability Theory) &amp;ndash; Statistics 515 (Stochastic Processes and Monte Carlo Methods)
Fall 2023: &amp;ndash; Statistics 415 (Introduction to Mathematical Statistics)
Spring 2023: &amp;ndash; Statistics 415 (Introduction to Mathematical Statistics) &amp;ndash; Statistics 540 (Statistical Computing)
Fall 2022: &amp;ndash; Statistics 415 (Introduction to Mathematical Statistics)
Spring 2022: &amp;ndash;Statistics 415 (Introduction to Mathematical Statistics)</description>
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