- Ba, I., & Coeurjolly, J.‐F. (2022a). Short tutorial related to the paper “Inference for low and high dimensional inhomogeneous Gibbs point processes”. Scandinavian Journal of Statistics.
Paper not yet in RePEc: Add citation now
- Ba, I., & Coeurjolly, J.‐F. (2022b). Supplement to “Inference for low and high dimensional inhomogeneous Gibbs point processes”. Scandinavian Journal of Statistics.
Paper not yet in RePEc: Add citation now
- Baddeley, A., & Turner, R. (2000). Practical maximum pseudolikelihood for spatial point patterns: (with discussion). Australian & New Zealand Journal of Statistics, 42, 283–322.
Paper not yet in RePEc: Add citation now
Baddeley, A., Coeurjolly, J.‐F., Rubak, E., & Waagepetersen, R. (2014). Logistic regression for spatial Gibbs point processes. Biometrika, 101, 377–392.
- Baddeley, A., Rubak, E., & Turner, R. (2015). Spatial point patterns: Methodology and applications with R. Chapman and Hall/CRC Press.
Paper not yet in RePEc: Add citation now
- Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L. (2013). Valid post‐selection inference. The Annals of Statistics, 41, 802–837.
Paper not yet in RePEc: Add citation now
- Besag, J. (1978). Some methods of statistical analysis for spatial data. Bulletin International Statistical Institute, 47, 77–92.
Paper not yet in RePEc: Add citation now
- Billiot, J.‐M., Coeurjolly, J.‐F., & Drouilhet, R. (2008). Maximum pseudolikelihood estimator for exponential family models of marked Gibbs point processes. Electronic Journal of Statistics, 2, 234–264.
Paper not yet in RePEc: Add citation now
- Breheny, P., & Huang, J. (2011). Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. The Annals of Applied Statistics, 5, 232.
Paper not yet in RePEc: Add citation now
- Choiruddin, A., Coeurjolly, J.‐F., & Letué, F. (2018). Convex and non‐convex regularization methods for spatial point processes intensity estimation. Electronic Journal of Statistics, 12, 1210–1255.
Paper not yet in RePEc: Add citation now
- Choiruddin, A., Coeurjolly, J.‐F., & Waagepetersen, R. (2021). Information criteria for inhomogeneous spatial point processes. Australian & New Zealand Journal of Statistics, 63, 119–143.
Paper not yet in RePEc: Add citation now
- Coeurjolly, J.‐F. (2015). Almost sure behavior of functionals of stationary Gibbs point processes. Statistics & Probability Letters, 97, 241–246.
Paper not yet in RePEc: Add citation now
- Coeurjolly, J.‐F., & Drouilhet, R. (2010). Asymptotic properties of the maximum pseudo‐likelihood estimator for stationary Gibbs point processes including the Lennard‐Jones model. Electronic Journal of Statistics, 4, 677–706.
Paper not yet in RePEc: Add citation now
- Coeurjolly, J.‐F., & Lavancier, F. (2017). Parametric estimation of pairwise Gibbs point processes with infinite range interaction. Bernoulli, 23, 1299–1334.
Paper not yet in RePEc: Add citation now
Coeurjolly, J.‐F., & Rubak, E. (2013). Fast covariance estimation for innovations computed from a spatial Gibbs point process. Scandinavian Journal of Statistics, 40, 669–684.
Coeurjolly, J.‐F., Møller, J., & Waagepetersen, R. (2017). A tutorial on palm distributions for spatial point processes. International Statistical Review, 85, 404–420.
- Condit, R. (1998). Tropical forest census plots: Methods and results from Barro Colorado Island, Panama and a comparison with other plots. Springer Science & Business Media.
Paper not yet in RePEc: Add citation now
- Daley, D. J., & Vere‐Jones, D. (2007). An introduction to the theory of point processes: Volume II: General theory and structure. Springer Science & Business Media.
Paper not yet in RePEc: Add citation now
Daniel, J., Horrocks, J., & Umphrey, G. J. (2018). Penalized composite likelihoods for inhomogeneous Gibbs point process models. Computational Statistics & Data Analysis, 124, 104–116.
- Dereudre, D., & Lavancier, F. (2009). Campbell equilibrium equation and pseudo‐likelihood estimation for non‐hereditary Gibbs point processes. Bernoulli, 15, 1368–1396.
Paper not yet in RePEc: Add citation now
- Dereudre, D., & Lavancier, F. (2017). Consistency of likelihood estimation for Gibbs point processes. The Annals of Statistics, 45, 744–770.
Paper not yet in RePEc: Add citation now
- Dereudre, D., Drouilhet, R., & Georgii, H.‐O. (2012). Existence of Gibbsian point processes with geometry‐dependent interactions. Probability Theory and Related Fields, 153, 643–670.
Paper not yet in RePEc: Add citation now
Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348–1360.
- Fan, J., & Peng, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. The Annals of Statistics, 32, 928–961.
Paper not yet in RePEc: Add citation now
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1.
- Friedman, J., Hastie, T., Höfling, H., & Tibshirani, R. (2007). Pathwise coordinate optimization. The Annals of Applied Statistics, 1, 302–332.
Paper not yet in RePEc: Add citation now
Gao, X., & Song, P. X.‐K. (2010). Composite likelihood bayesian information criteria for model selection in high‐dimensional data. Journal of the American Statistical Association, 105, 1531–1540.
- Georgii, H.‐O. (1979). Canonical Gibbs measures, volume 760 of lecture notes in mathematics.
Paper not yet in RePEc: Add citation now
- Georgii, H.‐O. (2011). Gibbs measures and phase transitions (Vol. 9). Walter de Gruyter.
Paper not yet in RePEc: Add citation now
Guan, Y., & Shen, Y. (2010). A weighted estimating equation approach for inhomogeneous spatial point processes. Biometrika, 97, 867–880.
Hengl, T., Sierdsema, H., Radović, A., & Dilo, A. (2009). Spatial prediction of species' distributions from occurrence‐only records: Combining point pattern analysis, ENFA and regression‐kriging. Ecological Modelling, 220, 3499–3511.
- Hoerl, A., & Kennard, R. (1988). Ridge regression. In Encyclopedia of Statistical Sciences? (Vol. 8). https://doi.org/10.1002/0471667196.ess2280.pub2.
Paper not yet in RePEc: Add citation now
- Hubbell, S. P., Condit, R. & Foster, R. B. (2005). Barro Colorado forest census plot data.
Paper not yet in RePEc: Add citation now
- Hubbell, S. P., Foster, R. B., O'Brien, S. T., Harms, K. E., Condit, R., Wechsler, B., Wright, S. J., & De Lao, S. L. (1999). Light‐gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science, 283, 554–557.
Paper not yet in RePEc: Add citation now
Hui, F. K., Warton, D. I., & Foster, S. D. (2015). Tuning parameter selection for the adaptive lasso using Eric. Journal of the American Statistical Association, 110, 262–269.
- Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. (2008). Statistical analysis and modelling of spatial point patterns (Vol. 70). John Wiley & Sons.
Paper not yet in RePEc: Add citation now
- Ivanoff, S., Picard, F., & Rivoirard, V. (2016). Adaptive Lasso and group‐Lasso for functional Poisson regression. The Journal of Machine Learning Research, 17, 1903–1948.
Paper not yet in RePEc: Add citation now
- Jensen, E. B. V., & Nielsen, L. S. (2001). A review on inhomogeneous Markov point processes. Lecture Notes‐Monograph Series, 37, 297–318.
Paper not yet in RePEc: Add citation now
Jensen, J. L., & Künsch, H. R. (1994). On asymptotic normality of pseudo likelihood estimates for pairwise interaction processes. Annals of the Institute of Statistical Mathematics, 46, 475–486.
- Jensen, J. L., & Møller, J. (1991). Pseudolikelihood for exponential family models of spatial point processes. The Annals of Applied Probability, 1, 445–461.
Paper not yet in RePEc: Add citation now
- Lee, J. D., Sun, D. L., Sun, Y., & Taylor, J. E. (2016). Exact post‐selection inference, with application to the lasso. The Annals of Statistics, 44, 907–927.
Paper not yet in RePEc: Add citation now
- Mase, S. (1995). Consistency of the maximum pseudo‐likelihood estimator of continuous state space Gibbsian processes. The Annals of Applied Probability, 5, 603–612.
Paper not yet in RePEc: Add citation now
- Møller, J., & Waagepetersen, R. P. (2003). Statistical inference and simulation for spatial point processes. Chapman and Hall/CRC Press.
Paper not yet in RePEc: Add citation now
Møller, J., & Waagepetersen, R. P. (2007). Modern statistics for spatial point processes. Scandinavian Journal of Statistics, 34, 643–684.
Rajala, T., Murrell, D., & Olhede, S. (2018). Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection. Journal of the Royal Statistical Society Series C, 67, 1237–1273.
- Reynaud‐Bouret, P. (2003). Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities. Probability Theory and Related Fields, 126, 103–153.
Paper not yet in RePEc: Add citation now
- Ripley, B. D. (1991). Statistical inference for spatial processes. Cambridge University Press.
Paper not yet in RePEc: Add citation now
- Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.
Paper not yet in RePEc: Add citation now
- Stein, M. L. (1999). Interpolation of spatial data: Some theory for kriging Springer Series in Statistics (1st ed.). Springer‐Verlag. http://gen.lib.rus.ec/book/index.php?md5=e2da912dee4e6b700d99ddb258ef8d4a.
Paper not yet in RePEc: Add citation now
- Taylor, J., & Tibshirani, R. (2018). Post‐selection inference for‐penalized likelihood models. Canadian Journal of Statistics, 46, 41–61.
Paper not yet in RePEc: Add citation now
- Team, R. C. (2019). R: A language and environment for statistical computing, version 3.3. 1. R Foundation for Statistical Computing.
Paper not yet in RePEc: Add citation now
- Thurman, A. L., & Zhu, J. (2014). Variable selection for spatial Poisson point processes via a regularization method. Statistical Methodology, 17, 113–125.
Paper not yet in RePEc: Add citation now
- Thurman, A. L., Fu, R., Guan, Y., & Zhu, J. (2015). Regularized estimating equations for model selection of clustered spatial point processes. Statistica Sinica, 25, 173–188.
Paper not yet in RePEc: Add citation now
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.
Paper not yet in RePEc: Add citation now
- Vasseur, T., Coeurjolly, J.‐F. and Dereudre, D. (2020). Existence of inhomogeneous Gibb point processes in the infinite volume. Preprint.
Paper not yet in RePEc: Add citation now
Waagepetersen, R. P. (2007). An estimating function approach to inference for inhomogeneous Neyman–Scott processes. Biometrics, 63, 252–258.
- Xanh, N. X., & Zessin, H. (1979). Integral and differential characterizations of the Gibbs process. Mathematische Nachrichten, 88, 105–115.
Paper not yet in RePEc: Add citation now
- Yue, Y., & Loh, J. M. (2015). Variable selection for inhomogeneous spatial point process models. Canadian Journal of Statistics, 43, 288–305.
Paper not yet in RePEc: Add citation now
- Zhang, C.‐H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38, 894–942.
Paper not yet in RePEc: Add citation now
Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418–1429.
- Zou, H., & Zhang, H. H. (2009). On the adaptive elastic‐net with a diverging number of parameters. The Annals of Statistics, 37, 1733.
Paper not yet in RePEc: Add citation now
- Zou, H., Hastie, T., & Tibshirani, R. (2007). On the Degrees of freedom of the Lasso. The Annals of Statistics, 35, 2173–2192.
Paper not yet in RePEc: Add citation now