fix some references (#2346)

arviz/plots/ecdfplot.py

@@ -98,9 +98,9 @@ def plot_ecdf(
 
     References
     ----------
-    .. [1] Säilynoja, T., Bürkner, P.C. and Vehtari, A., 2021. Graphical Test for
+    .. [1] Säilynoja, T., Bürkner, P.C. and Vehtari, A. (2022). Graphical Test for
         Discrete Uniformity and its Applications in Goodness of Fit Evaluation and
-        Multiple Sample Comparison. arXiv preprint arXiv:2103.10522.
+        Multiple Sample Comparison. Statistics and Computing, 32(32).
 
     Examples
     --------

arviz/plots/essplot.py

@@ -138,9 +138,9 @@ def plot_ess(
 
     References
     ----------
-    .. [1] Vehtari et al. (2019). Rank-normalization, folding, and
+    .. [1] Vehtari et al. (2021). Rank-normalization, folding, and
         localization: An improved Rhat for assessing convergence of
-        MCMC https://arxiv.org/abs/1903.08008
+        MCMC. Bayesian analysis, 16(2):667-718.
 
     Examples
     --------

arviz/plots/khatplot.py

@@ -149,8 +149,9 @@ def plot_khat(
 
     References
     ----------
-    .. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J.,
-        2019. Pareto Smoothed Importance Sampling. arXiv:1507.02646 [stat].
+    .. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J. (2024).
+        Pareto Smoothed Importance Sampling. Journal of Machine Learning
+        Research, 25(72):1-58.
 
     """
     if annotate:

arviz/plots/loopitplot.py

@@ -55,7 +55,7 @@ def plot_loo_pit(
         In this case, instead of overlaying uniform distributions, the beta ``hdi_prob``
         around the theoretical uniform CDF is shown. This approximation only holds
         for large S and ECDF values not very close to 0 nor 1. For more information, see
-        `Vehtari et al. (2019)`, `Appendix G <https://avehtari.github.io/rhat_ess/rhat_ess.html>`_.
+        `Vehtari et al. (2021)`, `Appendix G <https://avehtari.github.io/rhat_ess/rhat_ess.html>`_.
     ecdf_fill : bool, optional
         Use :meth:`matplotlib.axes.Axes.fill_between` to mark the area
         inside the credible interval. Otherwise, plot the

arviz/plots/mcseplot.py

@@ -109,7 +109,9 @@ def plot_mcse(
 
     References
     ----------
-    * Vehtari et al. (2019) see https://arxiv.org/abs/1903.08008
+    .. [1] Vehtari et al. (2021). Rank-normalization, folding, and
+        localization: An improved Rhat for assessing convergence of
+        MCMC. Bayesian analysis, 16(2):667-718.
 
     Examples
     --------

arviz/plots/rankplot.py

@@ -46,8 +46,8 @@ def plot_rank(
     indicates good mixing of the chains.
 
     This plot was introduced by Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter,
-    Paul-Christian Burkner (2019): Rank-normalization, folding, and localization: An improved R-hat
-    for assessing convergence of MCMC. arXiv preprint https://arxiv.org/abs/1903.08008
+    Paul-Christian Burkner (2021): Rank-normalization, folding, and localization:
+    An improved R-hat for assessing convergence of MCMC. Bayesian analysis, 16(2):667-718.
 
 
     Parameters

arviz/stats/diagnostics.py

@@ -135,10 +135,11 @@ def ess(
 
     References
     ----------
-    * Vehtari et al. (2019) see https://arxiv.org/abs/1903.08008
-    * https://mc-stan.org/docs/2_18/reference-manual/effective-sample-size-section.html
-      Section 15.4.2
-    * Gelman et al. BDA (2014) Formula 11.8
+    * Vehtari et al. (2021). Rank-normalization, folding, and
+        localization: An improved Rhat for assessing convergence of
+        MCMC. Bayesian analysis, 16(2):667-718.
+    * https://mc-stan.org/docs/reference-manual/analysis.html#effective-sample-size.section
+    * Gelman et al. BDA3 (2013) Formula 11.8
 
     See Also
     --------
@@ -246,7 +247,7 @@ def rhat(data, *, var_names=None, method="rank", dask_kwargs=None):
         Names of variables to include in the rhat report
     method : str
         Select R-hat method. Valid methods are:
-        - "rank"        # recommended by Vehtari et al. (2019)
+        - "rank"        # recommended by Vehtari et al. (2021)
         - "split"
         - "folded"
         - "z_scale"
@@ -277,12 +278,15 @@ def rhat(data, *, var_names=None, method="rank", dask_kwargs=None):
     greater than one indicate that one or more chains have not yet converged.
 
     Rank values are calculated over all the chains with ``scipy.stats.rankdata``.
-    Each chain is split in two and normalized with the z-transform following Vehtari et al. (2019).
+    Each chain is split in two and normalized with the z-transform following
+    Vehtari et al. (2021).
 
     References
     ----------
-    * Vehtari et al. (2019) see https://arxiv.org/abs/1903.08008
-    * Gelman et al. BDA (2014)
+    * Vehtari et al. (2021).  Rank-normalization, folding, and
+      localization: An improved Rhat for assessing convergence of
+      MCMC. Bayesian analysis, 16(2):667-718.
+    * Gelman et al. BDA3 (2013)
     * Brooks and Gelman (1998)
     * Gelman and Rubin (1992)
 

arviz/stats/stats.py

@@ -879,7 +879,8 @@ def psislw(log_weights, reff=1.0):
 
     References
     ----------
-    * Vehtari et al. (2015) see https://arxiv.org/abs/1507.02646
+    * Vehtari et al. (2024). Pareto smoothed importance sampling. Journal of Machine
+      Learning Research, 25(72):1-58.
 
     See Also
     --------