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Quasi-Monte Carlo (QMC) methods such as Halton sequences and modified Latin hypercube sampling are workhorse methods for integral approximation. Earlier studies explored the potential of sparse grid quadrature (SGQ), but SGQ suffers from negative weights. As an alternative to QMC and SGQ, we looked into the recently developed designed quadrature (DQ) method. DQ requires fewer nodes to get the same level of accuracy as QMC and SGQ, is as easy to implement, ensures positivity of weights, and can be created on any general polynomial space. We benchmarked DQ against QMC in a Monte Carlo and an empirical study. DQ outperformed QMC in all considered scenarios, is practice ready, and has potential to become the workhorse method for integral approximation.<\/jats:p>","DOI":"10.1093\/ectj\/utab023","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T18:06:23Z","timestamp":1626890783000},"page":"301-321","source":"Crossref","is-referenced-by-count":2,"title":["Designed quadrature to approximate integrals in maximum simulated likelihood estimation"],"prefix":"10.1093","volume":"25","author":[{"given":"Prateek","family":"Bansal","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Imperial College London , Exhibition Rd, South Kensington, London SW7 2BX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vahid","family":"Keshavarzzadeh","sequence":"additional","affiliation":[{"name":"Scientific Computing and Imaging Institute, University of Utah , 72 Central Campus Dr, Salt Lake City, UT 84112, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelo","family":"Guevara","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Civil, Universidad de Chile, Chile, Instituto Sistemas Complejos de Ingenier\u00eda , Av. 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