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A deep learning representation and spatial Bayesian cell-type deconvolution for spatial transcriptomics [PDF]
PeerJBackground Spatial transcriptomics provides unprecedented insights into gene expression in the spatial context of tissues; however, some mainstream techniques lack quantitative analysis of uncertainty.
Xiao Yang +4 more
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StatsThe literature on discrete valued time series is expanding very fast. Very often we see new models with very similar properties to the existing ones.
Dimitris Karlis +2 more
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On Some Stationary INAR(1) Processes with Compound Poisson Distributions
Revstat Statistical Journal, 2023 Aly and Bouzar ([2]) used the backward approach in presence of the binomial thinning operator to construct underdispersed stationary first-order autoregressive integer valued (INAR (1)) processes.
Emad-Eldin A. A. Aly, Nadjib Bouzar
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A New First-Order Integer-Valued Autoregressive Model with Bell Innovations
Entropy, 2021 A Poisson distribution is commonly used as the innovation distribution for integer-valued autoregressive models, but its mean is equal to its variance, which limits flexibility, so a flexible, one-parameter, infinitely divisible Bell distribution may be ...
Jie Huang, Fukang Zhu
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First-Order Integer-Valued Moving Average Process with Power Series Innovations
Journal of Statistical Theory and Applications (JSTA), 2020 In this paper, we introduce a first-order nonnegative integer-valued moving average process with power series innovations based on a Poisson thinning operator (PINMAPS(1)) for modeling overdispersed, equidispersed and underdispersed count time series ...
Eisa Mahmoudi, Ameneh Rostami
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Residual Analysis with Bivariate INAR(1) Models
Revstat Statistical Journal, 2018 In this paper we analyze forecasting errors made by random coefficients bivariate integer-valued autoregressive models of order one. These models are based on the thinning operator to support discreteness of data.
Predrag M. Popović +2 more
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Random Environment INAR Models of Higher Order
Revstat Statistical Journal, 2019 Two different random environment INAR models of higher order, RrNGINARmax(p) and RrNGINAR1(p), are introduced. Both of them are of variable order, which is not a random variable.
Aleksandar S. Nastić +2 more
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Estimation for random coefficient integer-valued autoregressive model under random environment
Advances in Difference Equations, 2019 A first-order random coefficient integer-valued autoregressive model based on the negative binomial thinning operator under r states random environment is introduced.
Yan Cui, Yun Y. Wang
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Bivariate Random Coefficient Integer-Valued Autoregressive Model Based on a ρ-Thinning Operator
AxiomsWhile overdispersion is a common phenomenon in univariate count time series data, its exploration within bivariate contexts remains limited. To fill this gap, we propose a bivariate integer-valued autoregressive model.
Chang Liu, Dehui Wang
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Randomness Test of Thinning Parameters for the NBRCINAR(1) Process
AxiomsNon-negative integer-valued time series are usually encountered in practice, and a variety of integer-valued autoregressive processes based on various thinning operators are commonly used to model these count data with temporal dependence. In this paper,
Shuanghong Zhang
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