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Statistical Inference for Renewal Processes

F. Comte, C. Duval
2017 Scandinavian Journal of Statistics  
The MISE corresponding to this strategy is given for fixed ∆ as well as for small ∆.  ...  Estimation of the interarrival distribution for renewal processes goes back to Vardi (1982) who proposed a consistent algorithm, based on the maximization of the likelihood.  ...  Then, the statistics R T is not ancillary.  ... 
doi:10.1111/sjos.12295 fatcat:guiehzd3pbft7bshgbhcgorjh4

Frequency Domain Statistical Inference for High-Dimensional Time Series [article]

Jonas Krampe, Efstathios Paparoditis
2022 arXiv   pre-print
The finite sample performance of the inference procedures proposed is investigated by means of simulations and applications to the construction of graphical interaction models for brain connectivity based  ...  In this paper, we develop inference procedures for such parameters in a high-dimensional, time series setup.  ...  In contexts and for inference problems different from those considered in this paper, de-biased or de-sparsified estimators, have been found to be useful tools for making statistical inference in a high-dimensional  ... 
arXiv:2206.02250v1 fatcat:7zu36np7arbafknmwqzqpr7lme

Approximate Bayesian inference for quantiles

David B. Dunson, Jack A. Taylor
2005 Journal of nonparametric statistics (Print)  
Properties of the substitution likelihood are investigated, strategies for prior elicitation are presented, and a general framework is proposed for quantile regression modeling.  ...  Posterior computation proceeds via a Metropolis algorithm that utilizes a normal approximation to the posterior.  ...  ACKNOWLEDGEMENTS The authors thank Shyamal Peddada and Zhen Chen for their critical reading of the manuscript. Thanks also to the Associate Editor and two referees for their helpful comments.  ... 
doi:10.1080/10485250500039049 fatcat:fmnppl4morgvjgitir5rqm6koe

Statistical inference of regulatory networks for circadian regulation

Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk
2014 Statistical Applications in Genetics and Molecular Biology  
We assess the accuracy of various state-of-the-art statistics and machine learning methods for reconstructing gene and protein regulatory networks in the context of circadian regulation.  ...  , it provides deeper insight into when and why non-linear methods fail to outperform linear ones, it offers improved guidelines on parameter settings in different inference procedures, and it suggests  ...  We are grateful to Andrew Millar, Alexander Pokhilko, and V. Anne Smith for helpful discussions.  ... 
doi:10.1515/sagmb-2013-0051 pmid:24864301 fatcat:6po5swfc25a6lisphtyrtyamwy

Asymptotic posterior normality for multiparameter problems

Ruby C. Weng, Wen-Chi Tsai
2008 Journal of Statistical Planning and Inference  
For asymptotic posterior normality in the one-parameter cases, Weng [2003. On Stein's identity for posterior normality. Statist.  ...  Sinica 13, 495--506] proposed to use a version of Stein's Identity to write the posterior expectations for functions of a normalized quantity in a form that is more transparent and can be easily analyzed  ...  Next, for (L2) it suffices to show that the supremum (over { : z t b t }) for each component of the matrix I k + (B t ) −1 ((j 2 t /j i j j )( ij ))B −1 t converges to zero in P 0 -probability.  ... 
doi:10.1016/j.jspi.2008.03.034 fatcat:qghvqlso5jb27m2a44roqwfgni

Bayesian inference for Poisson and multinomial log-linear models

Jonathan J. Forster
2010 Statistical Methodology  
For multinomial data, Lindley (1964) showed that this approach leads to valid Bayesian posterior inferences when the prior density for the Poisson cell means factorises in a particular way.  ...  We generalise this result to analyse multinomial or product multinomial data using a Poisson model. Valid finite population inferences are also available.  ...  First, we derive conditions for the posterior distribution for β resulting from an improper uniform prior to be proper.  ... 
doi:10.1016/j.stamet.2009.12.004 fatcat:v3rwrjoaxnbgpn3dh2cck3wllu

Minimax designs for approximately linear regression

Douglas P. Wiens
1992 Journal of Statistical Planning and Inference  
In this case we give minimax designs for loss functions corresponding to the classical D-, A-, E-, Q-and G-optimality criteria.  ...  For loss functions which are monotonic functions of the mean squared error matrix, we derive a theory to guide in the construction of designs which minimize the maximum (over f) loss.  ...  It is convenient to parametrize the solutions by y/ye, which varies from 1, for the uniform design, to (q + 2)/q, for the degenerate design with all mass at ljxll = r.  ... 
doi:10.1016/0378-3758(92)90142-f fatcat:vnirdw7e2vewrkxhspwrzu5wk4

Bayesian variable and link determination for generalised linear models

Ioannis Ntzoufras, Petros Dellaportas, Jonathan J Forster
2003 Journal of Statistical Planning and Inference  
In this paper, we describe full Bayesian inference for generalised linear models where uncertainty exists about the structure of the linear predictor, the linear parameters and the link function.  ...  Choice of suitable prior distributions is discussed in detail and we propose an e cient reversible jump Markov chain Monte-Carlo algorithm for calculating posterior summaries.  ...  For convenience we usually assume them to be discrete uniform.  ... 
doi:10.1016/s0378-3758(02)00298-7 fatcat:idt3csvsqjfrjhs5iq7jekwgfy

Inference for high-dimensional varying-coefficient quantile regression

Ran Dai, Mladen Kolar
2021 Electronic Journal of Statistics  
In this work, we study high-dimensional varying-coefficient quantile regression models and develop new tools for statistical inference.  ...  Performing statistical inference in this regime is challenging due to the usage of model selection techniques in estimation.  ...  Acknowledgments We thank Rina Foygel Barber for numerous suggestions and detailed advice, as well as careful reading of various versions of the manuscript.  ... 
doi:10.1214/21-ejs1919 fatcat:mjelkk7yvfccblfxyvtot6sd64

An Analysis of Bayesian Inference for Nonparametric Regression

Dennis D. Cox
1993 Annals of Statistics  
One of the attractive features of the Bayesian approach is that in principle one can solve virtually any statistical decision or inference problem.  ...  The signal extraction approach to nonlinear regression and spline smoothing. J. Amer. Statist.  ... 
doi:10.1214/aos/1176349157 fatcat:twkxupycpfhqncto6i63bcdp2y

Practical valid inferences for the two-sample binomial problem

Michael P. Fay, Sally A. Hunsberger
2021 Statistics Survey  
For focus, we only examine non-asymptotic inferences, so that most of the p-values and confidence intervals are valid (i.e., exact) by construction.  ...  Yet there continues to be new work on this issue and no consensus solution.  ...  Acknowledgments The authors thank Erica Brittain and anonymous reviewers for comments that improved the paper.  ... 
doi:10.1214/21-ss131 fatcat:rxltzwpn5zb35hlnyzjhwxytoy

Approximate Slice Sampling for Bayesian Posterior Inference

Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth
2014 International Conference on Artificial Intelligence and Statistics  
While this introduces a bias in the stationary distribution, the computational savings allow us to draw more samples in a given amount of time and reduce sampling variance.  ...  In this paper, we advance the theory of large scale Bayesian posterior inference by introducing a new approximate slice sampler that uses only small mini-batches of data in every iteration.  ...  We see that the number of data points required to make the statistical decisions for those tests decreases as increases.  ... 
dblp:conf/aistats/DuBoisBWS14 fatcat:izzur5sucjedtlifpcpdats3lu

On the edgeworth expansion for the sum of a function of uniform spacings

R.J.M.M. Does, R. Helmers, C.A.J. Klaassen
1987 Journal of Statistical Planning and Inference  
This is done by proving Cramer's condition for statistics of the general type f(X) under quite weak assumptions on the random variable X and the function f: IR"'--. IRk.  ...  This condition is shown to hold under an easily verifiable and mild assumption on the function g.  ...  Acknowledgements The authors are very grateful to the editor and the associate editor for their comments and suggestions. R.J.M.M. Does et al. I Uniform spacings  ... 
doi:10.1016/0378-3758(87)90108-x fatcat:jlfvu7olzraftdkln43rcpal3a

Bayesian inference for stochastic differential equation mixed effects models of a tumour xenography study

Umberto Picchini, Julie Lyng Forman
2019 Journal of the Royal Statistical Society, Series C: Applied Statistics  
We consider Bayesian inference for stochastic differential equation mixed effects models (SDEMEMs) exemplifying tumor response to treatment and regrowth in mice.  ...  Results from the case study and from simulations indicate that the SDEMEM is able to reproduce the observed growth patterns and that BSL is a robust tool for inference in SDEMEMs.  ...  We would like to thank the research team at the Center for Nanomedicine and Theranostics, DTU Nanotech, Denmark for providing the data for the case study and for introducing us to the problem of making  ... 
doi:10.1111/rssc.12347 fatcat:4jey4idvvjfdpjmazw3hk4tktu

Inference for Deterministic Simulation Models: The Bayesian Melding Approach

David Poole, Adrian E. Raftery
2000 Journal of the American Statistical Association  
We also propose diagnostic checking, model validation, hypothesis testing and model selection methods, so that Bayesian melding provides a comprehensive framework for coherent statistical inference from  ...  Here we address the issue of more formal inference for such models.  ...  We are grateful to Thomas F. Albert for sustained research support, and to Stephen T. Buckland, Douglas S. Butterworth,  ... 
doi:10.2307/2669764 fatcat:zcwflhuzq5aotg2ezcth3uknk4
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