A. Jalilian

A. Jalilian

Statistician

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Analysis of multispecies point patterns by using multivariate log‐Gaussian Cox processes

Published in Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016

Recommended citation: Waagepetersen, R., Guan, Y., Jalilian, A., & Mateu, J. (2016). "Analysis of multispecies point patterns by using multivariate log‐Gaussian Cox processes". Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(1), 77-96. https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12108

Fitting Skew Distributions to Iranian Auto Insurance Claim Data

Published in Applications & Applied Mathematics, 2017

Recommended citation: Kazemi, R., Jalilian, A., & Kohansal, A. (2017). Fitting Skew Distributions to Iranian Auto Insurance Claim Data. Applications & Applied Mathematics, 12(2). https://www.researchgate.net/profile/Abdollah_Jalilian/publication/322083376_Fitting_Skew_Distributions_to_Iranian_Auto_Insurance_Claim_Data/links/5a43a4c5458515f6b052be98/Fitting-Skew-Distributions-to-Iranian-Auto-Insurance-Claim-Data.pdf

A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany

Published in Stochastic Environmental Research and Risk Assessment, 2021

Recommended citation: Jalilian, A., & Mateu, J. (2021). " A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany". Stochastic Environmental Research and Risk Assessment, 35(4), 797-812. https://link.springer.com/article/10.1007/s00477-021-02003-2

Bayesian spatial analysis of age differences and geographical variations in illicit-drug-related mortality in the Islamic Republic of Iran

Published in Eastern Mediterranean Health Journal, 2023

Recommended citation: Rostami, M., Jalilian, A., Ghadirzadeh, M. R., Nazparvar, B., Rezaei-Zangeneh, R., & Karamouzian, M. (2023). Bayesian spatial analysis of age differences and geographical variations in illicit-drug-related mortality in the Islamic Republic of Iran. Eastern Mediterranean Health Journal, 29(1). https://www.emro.who.int/in-press/research/bayesian-spatial-analysis-of-age-differences-and-geographical-variations-in-illicit-drug-related-mortality-in-the-islamic-republic-of-iran.html

talks

Product density functions of Neyman-Scott point processes

Published:

We provide a general formula for the product density functions of a Neyman-Scott process. Upper bounds and relation with the characteristic function of the process’ kernel are also discussed. In addition, closed expressions are presented for the third and fourth order product densities of the Thomas process.

Estimation of the pair correlation function

Published:

The pair correlation function is a key summary statistic in analyzing spatial point patterns. It has become an important tool in forestry, cosmology and other disciplines. Kernel smoothing methods are often used to estimate the pair correlation function, similar to kernel estimation of probability densities. However, the kernel estimator with symmetric bounded support kernels suffers from the well-known boundary bias problem and the bias and variance of a kernel estimate depend critically on the choice of kernel bandwidth. In the present research project, we aim to use asymmetric kernel functions in order to eliminate the boundary bias issue and develop a feasible methodology for optimal choice of the bandwidth.

Generalized K function for spatial point processes

Published:

We introduce a generalized version of the well know Ripley’s K function in order to obtain higher order characteristics for spatial point processes. The generalized K function admits a simple closed form for the Poisson process and a tractable analytical expression is obtained for the third order K function in case of the planar Thomas process. An unbiased nonparametric estimator for the generalized K function is also discussed.

A model for time series of spatial point patterns

Published:

A spatial point pattern $\mathbf{x}$ on $S\subset\mathbb{R}^{d}$, $d\geq2$, is a locally finite subset of $S$; i.e. for any bounded Borel set $B\subset S$, $\mathbf{x}\cap B$ if finite. Let $\mathcal{X}$ be the set of all point patterns on $S$ and $\mathcal{N}$ denotes the Borel $\sigma$-algebra of subsets of $\mathcal{X}$. Then a random object $X:(\Omega,\mathcal{F},\mathbb{P})\to(\mathcal{X},\mathcal{N},P_{X})$ is called a spatial point process on $S$. Assume that for each $t\in\mathbb{Z}$, $X_{t}$ is a spatial point process on $S$. Then ${X_t: t\in\mathbb{Z}}$ is a $\mathcal{X}$-valued time series that can not be explained in the classical time series framework. Such time series are encountered in various applications. In the present work, a framework for analyzing time series of spatial point patterns is developed and a simple model for the population dynamic in the BCI plot is introduced. In addition, parameter estimation for the proposed model is discussed.

A deep learning approach to modeling spatial point patterns of locations of different tree species in a tropical rainforest

Published:

Spatial point patterns of locations of trees in a rainforest are influenced by environmental conditions and many known and unknown ecological processes that are not directly observable. In this talk we propose a multilayer perceptrons model in order to relate the spatial patterns of trees to observed environmental variables and latent random fields, which accounts for all unobserved influential factors. We use the variational autoencoder approach to fit the proposed model and estimate (encode) the generative latent random fields.

A spatio-temporal model for locations of trees in the Barro Colorado Island plot

Published:

Locations of alive trees in the 50-hectare permanent study plot in the tropical rain forest of Barro Colorado Island (BCI), Gatun Lake, Panama, have been recorded in 8 consecutive censuses since 1980. In each census, new trees tend to grow near existing trees in the preceding census due to seed dispersal mechanisms and favorable topography and soil conditions. On the other hand, the elimination of trees from one census to the next seems to occur more frequently in dense areas where there are competitions for resources and light among trees. We introduce a spatio-temporal model for population dynamics in the BCI plot and discuss parameter estimation methods and model checking tools for the introduced model.

teaching

Advanced Probability

Graduate course, Razi University, Department of Statistics, 2020

احتمال پیشرفته
یکشنبه 17:45 و سه‌شنبه 12:15

C++ Programming

Undergraduate course, Razi University, Department of Statistics, 2020

مبانی کامپیوتر و برنامه‌نویسی (برنامه‌سازی)
گروه 02: شنبه 13:15 و چهارشنبه 15:30 (گردشی)
گروه 10: سه‌شنبه 15:30 و چهارشنبه 18:45 (گردشی)

Biostatistics Workshop

Undergraduate course, Razi University, Department of Statistics, 2020

کارگاه آمارزیستی
یکشنبه 10:15

English for Students of Statistics

Undergraduate course, Razi University, Department of Statistics, 2021

زبان تخصصی برای دانشجویان آمار
چهارشنبه 11:15