{"id":39,"date":"2015-10-26T14:57:47","date_gmt":"2015-10-26T18:57:47","guid":{"rendered":"http:\/\/sites.lsa.umich.edu\/sstoev\/?page_id=39"},"modified":"2019-03-01T14:12:32","modified_gmt":"2019-03-01T19:12:32","slug":"software","status":"publish","type":"page","link":"https:\/\/sites.lsa.umich.edu\/sstoev\/software\/","title":{"rendered":"Software"},"content":{"rendered":"<h3>Code for Simulation and Estimation in Various Stochastic Models<\/h3>\n<table style=\"border-collapse: collapse;width: 100.073%;height: 417px\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 30%\">\n<figure id=\"attachment_158\" aria-describedby=\"caption-attachment-158\" style=\"width: 250px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf03.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-158\" title=\"Fractional Brownian field (H=0.3)\" src=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf03.gif\" alt=\"Fractional Brownian field (H=0.3)\" width=\"250\" height=\"250\" \/><\/a><figcaption id=\"caption-attachment-158\" class=\"wp-caption-text\">Anisotropic Fractional Brownian field with self-similarity parameter H=0.3 and discrete spectrum.<\/figcaption><\/figure>\n<div class=\"mceTemp\"><\/div>\n<\/td>\n<td style=\"width: 30%\">\n<figure id=\"attachment_152\" aria-describedby=\"caption-attachment-152\" style=\"width: 250px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-152\" title=\"Fractional Brownian field (H=0.7)\" src=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf.gif\" alt=\"Fractional Brownian field (H=0.7)\" width=\"250\" height=\"250\" \/><\/a><figcaption id=\"caption-attachment-152\" class=\"wp-caption-text\">Anisotropic Fractional Brownian field with self-similarity parameter H=0.7 and discrete spectrum.<\/figcaption><\/figure><\/td>\n<td style=\"width: 30%;text-align: left\">\n<figure id=\"attachment_154\" aria-describedby=\"caption-attachment-154\" style=\"width: 250px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf09.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-154\" title=\"Fractional Brownian field (H=0.9)\" src=\"http:\/\/sites.lsa.umich.edu\/sstoev\/wp-content\/uploads\/sites\/323\/2019\/03\/fBf09.gif\" alt=\"Fractional Brownian field (H=0.9)\" width=\"250\" height=\"250\" \/><\/a><figcaption id=\"caption-attachment-154\" class=\"wp-caption-text\">Anisotropic Fractional Brownian field with self-similarity parameter H=0.9 and discrete spectrum.<\/figcaption><\/figure>\n<p>&nbsp;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"mceTemp\"><\/div>\n<div class=\"mceTemp\"><\/div>\n<p><b>Long Range Dependence<\/b><\/p>\n<ul>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/lass.zip\">LASS<\/a>: Local Analysis of Self-Similarity &#8211; a collection for the estimation of the Hurst long-range dependence parameter as a function of &#8220;time&#8221;. Tailored and intendend for the analysis of Internet traffic data.<br \/>\n<b>Note:<\/b> This code has not been updated recently.<\/li>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/FFT-sim.zip\">FFT-sim<\/a>: FFT-based tools for the efficient simulation of LFSM and FARIMA with stable innovations.<\/li>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/ffgn.m\">ffgn.m<\/a>: An routine for <i>exact<\/i> and <i>efficient <\/i>simulation of fGn (fractional Gaussian noise) time series. Hence, exact discretized paths of fBm &#8211; fractional Brownian motion &#8211; can be generated. The code uses <i>circulant embedding<\/i> and is written jointly with Yingchun Zhou (Jasmine), zhouyc@math.bu.edu.<\/li>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/Hest.zip\">Hest<\/a>: Wavelet based tools for the estimation of the Hurst LRD parameter H.<\/li>\n<\/ul>\n<p><b>Extremes<\/b><\/p>\n<ul>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/maxspec.zip\">max spectrum<\/a>: An estimator for the heavy tail exponent of a distribution based on scaling of block-maxima. Incorporates a method for the automated selection of the <i>extreme<\/i> cut-off.<br \/>\n<b>Note:<\/b> Main routine <i>maxss_3.m<\/i>.<\/li>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/maxspec.zip\">extremal index<\/a>: An estimator for the extremal index based on the <i>max spectrum<\/i> and resampling.<br \/>\n<b>Note:<\/b> Main routine <i>max_eind_1.m<\/i>.<\/li>\n<li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~sstoev\/code\/maxlin.zip\">max linear<\/a>: an R-package for exact conditional sampling from max-linear models courtesy of <a href=\"http:\/\/www.stat.lsa.umich.edu\/~yizwang\/software\/maxLinear\/index.html\">Yizao Wang<\/a>.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Code for Simulation and Estimation in Various Stochastic Models &nbsp; Long Range Dependence LASS: Local Analysis of Self-Similarity &#8211; a collection for the estimation of the Hurst long-range dependence parameter as a function of &#8220;time&#8221;. Tailored and intendend for the analysis of Internet traffic data. Note: This code has not been updated recently. FFT-sim: FFT-based [&hellip;]<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-39","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/pages\/39","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/comments?post=39"}],"version-history":[{"count":7,"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/pages\/39\/revisions"}],"predecessor-version":[{"id":163,"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/pages\/39\/revisions\/163"}],"wp:attachment":[{"href":"https:\/\/sites.lsa.umich.edu\/sstoev\/wp-json\/wp\/v2\/media?parent=39"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}