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Dynamic linear models (DLM) offer a very generic framework to analyse time series data. Many classical time series models can be formulated as DLMs, including ARMA models and standard multiple linear regression models. The models can be seen as general regression models where the coefficients can vary in time. In addition, they allow for a state space representation and a formulation as hierarchical statistical models, which in turn is the key for efficient estimation by Kalman formulas and by M...

This paper studies inference in observational studies with time-varying adoption of treatment. The main restriction underlying our analysis is that the time at which each unit adopts treatment follows a Cox proportional hazards model. This assumption permits the time at which each unit adopts treatment to depend on observed characteristics, but restricts the probability of multiple units adopting treatment simultaneously to be zero. In this context, we study randomization tests of a "sharp" null...

NIST SP800-22 is one of the widely used statistical testing tools for pseudorandom number generators (PRNGs). This tool consists of 15 tests (one-level tests) and two additional tests (two-level tests). Each of one-level tests provides one or more pvalues. The two-level tests measure the uniformity of the obtained pvalues for a fixed one-level test. One of the two-level tests is to categorize the pvalues into ten intervals of equal length, and apply a chi-squared goodness-of-fit test.Th...

Growth curve modeling should ideally be flexible, computationally feasible and allow for the inclusion of co-variates for better predictability and mechanistic explanations. The original shape invariant growth curve model, motivated by epidemiological research on the evolution of pubertal heights over time, fits the underlying shape function for height over age and estimates subject-specific deviations from this curve in terms of size, tempo, and velocity using maximum likelihood. This approach ...

We propose a method of testing the shift between mean vectors of two multivariate Gaussian random variables in a high-dimensional setting incorporating the possible dependency and allowing p > n This method is a combination of two well-known tests: the Hotelling test and the Simes test. The tests are integrated by sampling several dimensions at each iteration, testing each using the Hotelling test, and combining their results using the Simes test. We prove that this procedure is valid asympto...

We propose a new approach for scaling prior to cluster analysis based on the concept of pooled variance. Unlike available scaling procedures such as the standard deviation and the range, our proposed scale avoids dampening the beneficial effect of informative clustering variables. We confirm through an extensive simulation study and applications to well known real data examples that the proposed scaling method is safe and generally useful. Finally, we use our approach to cluster a high dimension...

A widespread challenge in statistics is that non-random participation in study samples can result in biased estimates of parameters of interest. To address this problem, we present a computationally efficient and non-parametric interval estimator for a class of population parameters encompassing population means, ordinary least squares and instrumental variables estimands which makes minimal assumptions about the selection mechanism. Using results from stochastic programming, we derive valid con...

Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchad\'e.

This is a contribution for the discussion on "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchad\'e to appear in the Journal of the Royal Statistical Society Series B.

Under standard prior distributions, fitted probabilities from Bayesian multinomial probit models can depend strongly on the choice of a base category, which is used to identify the model. This paper proposes a novel identification strategy and prior distribution for the model parameters that makes the prior symmetric with respect to relabeling the outcome categories. Further, our new prior allows an efficient Gibbs algorithm that samples rank-deficient covariance matrices without resorting to Me...

This paper considers robust modeling of the survival time for cancer patients. Accurate prediction can be helpful for developing therapeutic and care strategies. We propose a unified Expectation-Maximization approach combined with the L1-norm penalty to perform variable selection and obtain parameter estimation simultaneously for the accelerated failure time model with right-censored survival data. Our approach can be used with general loss functions, and reduces to the well-known Buckley-James ...

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