Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Sayantee Jana"'
Publikováno v:
Malaria Journal, Vol 21, Iss 1, Pp 1-10 (2022)
Abstract Background India has a substantial burden of malaria, concentrated in specific areas and population groups. Spatio-temporal modelling of deaths due to malaria in India is a critical tool for identifying high-risk groups for effective resourc
Externí odkaz:
https://doaj.org/article/2cd19d67418147099fee9e382af7e522
Autor:
Sayantee Jana, Mitchell Sutton, Tatyana Mollayeva, Vincy Chan, Angela Colantonio, Michael David Escobar
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
BackgroundMultiple testing procedures (MTP) are gaining increasing popularity in various fields of biostatistics, especially in statistical genetics. However, in injury surveillance research utilizing the growing amount and complexity of health-admin
Externí odkaz:
https://doaj.org/article/f096c4521f2147aa98ed6061e1892062
Autor:
Tatyana Mollayeva, Mitchell Sutton, Vincy Chan, Angela Colantonio, Sayantee Jana, Michael Escobar
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
Abstract The use of precision medicine is poised to increase in complex injuries such as traumatic brain injury (TBI), whose multifaceted comorbidities and personal circumstances create significant challenges in the domains of surveillance, managemen
Externí odkaz:
https://doaj.org/article/3cf1bdff4af24adea7ed1440f25e70d6
Publikováno v:
Journal of Statistical Planning and Inference. 225:219-242
Autor:
Jemila S. Hamid, Sayantee Jana
Publikováno v:
Recent Developments in Multivariate and Random Matrix Analysis ISBN: 9783030567729
The Growth Curve Model (GCM) is a Generalized Multivariate Analysis of Variance (GMANOVA) model especially useful in the analysis of longitudinal data, growth curves as well as other response curves. The model is a natural extension of the classical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0156800832fae381729ed571a3bb8092
https://doi.org/10.1007/978-3-030-56773-6_7
https://doi.org/10.1007/978-3-030-56773-6_7
Publikováno v:
Sankhya B. 82:34-69
Existing methods for estimating the parameters of the Growth Curve Model (GCM) rely on the assumption that the underlying distribution for the error terms is multivariate normal. However, we often come across skewed data in practical applications; an
Publikováno v:
Journal of Applied Statistics. 46:814-834
Traditional inference on the growth curve model (GCM) requires ‘small p large n’ (n≫p) and cannot be applied in high-dimensional scenarios, where we often encounter singularity. Several methods are proposed to tackle the singularity problem, ho
Publikováno v:
Journal of Multivariate Analysis. 166:111-128
The Growth Curve Model (GCM) assumes the same shape of profiles for each group, where group means are assumed to be represented by polynomials of the same degree. The model, therefore, is inappropriate when analyzing data from studies involving group
Autor:
Angela Colantonio, Sayantee Jana, Mitchell Sutton, Tatyana Mollayeva, Vincy Chan, Michael Escobar
Publikováno v:
Scientific Reports
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
The use of precision medicine is poised to increase in complex injuries such as traumatic brain injury (TBI), whose multifaceted comorbidities and personal circumstances create significant challenges in the domains of surveillance, management, and en
Publikováno v:
Statistical Methods & Applications. 26:273-292
Recent advances in technology have allowed researchers to collect large scale complex biological data, simultaneously, often in matrix format. In genomic studies, for instance, measurements from tens to hundreds of thousands of genes are taken from i