ترغب بنشر مسار تعليمي؟ اضغط هنا

ergm 4.0: New features and improvements

160   0   0.0 ( 0 )
 نشر من قبل Pavel Krivitsky
 تاريخ النشر 2021
  مجال البحث الاحصاء الرياضي
والبحث باللغة English




اسأل ChatGPT حول البحث

The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the functionality and performance improvements in the 2021 ergm 4.0 release. These include more flexible handling of nodal covariates, operator terms that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, performance enhancements to the Markov chain Monte Carlo and maximum likelihood estimation algorithms, broader and faster searching for networks with certain target statistics using simulated annealing, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergms functionality to other network data types and structural features, and the robust set of online resources that support the statnet development process and applications.

قيم البحث

اقرأ أيضاً

Theano is a linear algebra compiler that optimizes a users symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks d emonstrating Theanos performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequ ence-to-sequence modeling. The project has grown rapidly and now covers a wide range of speech processing applications. Now ESPnet also includes text to speech (TTS), voice conversation (VC), speech translation (ST), and speech enhancement (SE) with support for beamforming, speech separation, denoising, and dereverberation. All applications are trained in an end-to-end manner, thanks to the generic sequence to sequence modeling properties, and they can be further integrated and jointly optimized. Also, ESPnet provides reproducible all-in-one recipes for these applications with state-of-the-art performance in various benchmarks by incorporating transformer, advanced data augmentation, and conformer. This project aims to provide up-to-date speech processing experience to the community so that researchers in academia and various industry scales can develop their technologies collaboratively.
81 - Kris Sankaran 2021
Many modern datasets dont fit neatly into $n times p$ matrices, but most techniques for measuring statistical stability expect rectangular data. We study methods for stability assessment on non-rectangular data, using statistical learning algorithms to extract rectangular latent features. We design controlled simulations to characterize the power and practicality of competing approaches. This motivates new strategies for visualizing feature stability. Our stability curves supplement the direct analysis, providing information about the reliability of inferences based on learned features. Finally, we illustrate our approach using a spatial proteomics dataset, where machine learning tools can augment the scientists workflow, but where guarantees of statistical reproducibility are still central. Our raw data, packaged code, and experimental outputs are publicly available.
We investigate a Poisson sampling design in the presence of unknown selection probabilities when applied to a population of unknown size for multiple sampling occasions. The fixed-population model is adopted and extended upon for inference. The compl ete minimal sufficient statistic is derived for the sampling model parameters and fixed-population parameter vector. The Rao-Blackwell version of population quantity estimators is detailed. An application is applied to an emprical population. The extended inferential framework is found to have much potential and utility for empirical studies.
126 - Suvra Pal , Souvik Roy 2019
In this paper, we develop a new estimation procedure based on the non-linear conjugate gradient (NCG) algorithm for the Box-Cox transformation cure rate model. We compare the performance of the NCG algorithm with the well-known expectation maximizati on (EM) algorithm through a simulation study and show the advantages of the NCG algorithm over the EM algorithm. In particular, we show that the NCG algorithm allows simultaneous maximization of all model parameters when the likelihood surface is flat with respect to a Box-Cox model parameter. This is a big advantage over the EM algorithm, where a profile likelihood approach has been proposed in the literature that may not provide satisfactory results. We finally use the NCG algorithm to analyze a well-known melanoma data and show that it results in a better fit.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا