19 papers · ranked by Valyu relevance
Geyu Weng, Kelsey Clark, Amir Akbarian, Behrad Noudoost + 1 more
'Neda Nategh'] To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional…
Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner
Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron’s spiking, one might attempt to seek peaks in the spike train’s power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery…
Sean Bellew, Ian Flint, Yan Wang
Poisson processes have become a prominent tool in species distribution modelling when analysing citizen science data based on presence records. This study examines four distinct statistical approaches, each of which utilises a different approximation to fit a Poisson point process. These include two Poisson regressions…
Andrew S. Perley, Matthew Martinez, Tommaso Mercadante, Sabrina Liu + 1 more
The dynamics of heartbeat intervals provide important insights into cardiovascular and autonomic nervous system function. Conventional analytical approaches often use fixed-window averaging, which can obscure rapid changes and reduce temporal resolution. Point process models address this limitation by operating in…
Kwaku Peprah Adjei, Philip S. Mostert, Jorge Sicacha-Parada, Emma Sofie Skarstein + 1 more
'Emma Sofie Skarstein' 'Robert B. O’Hara'] The quantity and types of biodiversity data being collected have increased in recent years. If we are to model and monitor biodiversity effectively, we need to respect how different data sets were collected, and effectively integrate these data types together. The framework…
Peter Guttorp, Janine Illian, Joel Kostensalo, Mikko Kuronen + 3 more
Norwegian Computing Center, Oslo, Norway School of Mathematics and Statistics, University of Glasgow, Glasgow, Scotland, UK Natural Research Institute Finland (Luke), Joensuu, Finland Natural Research Institute Finland (Luke), Helsinki, Finland Department of Mathematical Sciences, Chalmers University of Technology and…
Jakub Stoklosa, Wen-Han Hwang, David I. Warton, María Paula Fernández García
'María Paula Fernández García'] In regression modelling, measurement error models are often needed to correct for uncertainty arising from measurements of covariates/predictor variables. The literature on measurement error (or errors-in-variables) modelling is plentiful, however, general algorithms and software for…
Nicoletta D’Angelo, Giada Adelfio
The stopp R package deals with spatio-temporal point processes which might have occurred on the Euclidean space or on some specific linear networks such as roads of a city. The package contains functions to summarize, plot, and perform different kinds of analyses on point processes, mainly following the methods…
Charlotte M. Jones‐Todd, Alec B. M. van Helsdingen
Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log-Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point-referenced data. In addition, point pattern models where events…
Haoran Chen, Robert F. Murphy
Recent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture…
Sean C. Anderson, Eric J. Ward, Philina A. English, Lewis A. K. Barnett
Geostatistical data—spatially referenced observations related to some continuous spatial phenomenon— are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian…
Andi Kresna Jaya, Nurtiti Sunusi, Erna Tri Herdiani
The point process model effectively represents the number of random events occurring over time through its intensity function. When events are of two types, a bivariate point process allows simultaneous analysis of each event’s intensity. This study develops a conditional intensity model for a non-homogeneous bivariate…
Edward Appau Nketiah, Chenlong Li, Weihua Yang, Yingchuan Jing + 2 more
Space-time self-exciting point process models are introduced to capture the clustering features in crime datasets. It does particularly well in modeling social network datasets, crime and security datasets, financial datasets, and seismic datasets. However, there has been limited analysis of large crime datasets using…
Zihao Wen, David L. Dowe, Abhijit Mandal, Suneel Babu Chatla
Species distribution modeling is fundamental to biodiversity, evolution, conservation science, and the study of invasive species. Given environmental data and species distribution data, model selection techniques are frequently used to help identify relevant features. Existing studies aim to find the relevant features…
Claudio Heinrich, Thordis L. Thorarinsdottir, Peter Guttorp, Max Schneider
'Max Schneider'] We introduce a class of proper scoring rules for evaluating spatial point process forecasts based on summary statistics. These scoring rules rely on Monte-Carlo approximations of expectations and can therefore easily be evaluated for any point process model that can be simulated. In this regard, they…
Arthur Newbury
Estimating underlying cooccurrence relationships between pairs of species has long been a challenging task in ecology as the extent to which species actually cooccur is partially dependent on their prevalences. While recent work has taken large steps towards solving this problem, the next question is how to assess the…
Peng Ding
| | Acronyms | | xiii | | --- | --- | --- | --- | | | Symbols | | xv | | | Useful R packages | | xvii | | Preface | | | xix | | I | Introduction | | 1 | | 1 | Motivations for Statistical Models | | 3 | | 1.1 | | Data and statistical models | 3 | | 1.2 | | Why linear models? | 5 | | 2 | | Ordinary Least Squares (OLS)…
Guilherme Parreira da Silva, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro + 1 more
'Ricardo Rasmussen Petterle' 'Paulo Justiniano Ribeiro' 'Wagner Hugo Bonat'] Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely…
Anna Ly, Rune Haubo Bojesen Christensen, Douglas Bates, Martin Maechler + 1 more
The lme4 R package can be used to fit generalized linear mixed models (GLMMs), which extend the class of linear mixed models (LMMs). The two main extensions provided by GLMMs are (1) allowing for the conditional distribution of the response given the random effects to be non-Gaussian (e.g. binomial, Poisson) and (2)…