| 1. |
- Bolin, David, 1983, et al.
(author)
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Statistical Prediction of Global Sea Level From Global Temperature
- 2015
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In: Statistica Sinica. - : Statistica Sinica (Institute of Statistical Science). - 1017-0405. ; 25:1, s. 351-367
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Journal article (peer-reviewed)abstract
- Sea level rise is a threat to many coastal communities, and projection of future sea level for different climate change scenarios is an important societal task In this paper, we first construct a time series regression model to predict global sea level from global temperature. The model is fitted to two sea level data sets (with and without corrections for reservoir storage of water) and three temperature data sets. The effect of smoothing before regression is also studied. Finally, we apply a novel methodology to develop confidence bands for the projected sea level, simultaneously for 2000-2100, under different scenarios, using temperature projections from the latest climate modeling experiment. The main finding is that different methods for sea level projection, which appear to disagree, have confidence intervals that overlap, when taking into account the different sources of variability in the analyses.
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| 2. |
- Cardoso, Gabriel, et al.
(author)
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Particle-based, Rapid Incremental Smoother Meets Particle Gibbs
- 2023
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In: Statistica sinica. - : Institute of Statistical Science. - 1017-0405 .- 1996-8507. ; :1
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Journal article (peer-reviewed)abstract
- The particle-based, rapid incremental smoother (PARIS) is a sequential Monte Carlo technique allowing for efficient online approximation of expectations of additive functionals under Feynman–Kac path distributions. Under weak assumptions, the algorithm has linear computational complexity and limited memory requirements. It also comes with a number of non-asymptotic bounds and convergence results. However, being based on self-normalised importance sampling, the PARIS estimator is biased; its bias is inversely proportional to the number of particles but has been found to grow linearly with the time horizon under appropriate mixing conditions. In this work, we propose the Parisian particle Gibbs (PPG) sampler, whose complexity is essentially the same as that of the PARIS and which significantly reduces the bias for a given computational complexity at the price of a modest increase in the variance. This method is a wrapper in the sense that it uses the PARIS algorithm in the inner loop of particle Gibbs to form a bias-reduced version of the targeted quantities. We substantiate the PPG algorithm with theoretical results, including new bounds on bias and variance as well as deviation inequalities. We illustrate our theoretical results with numerical experiments supporting our claims.
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| 3. |
- Fackle-Fornius, Ellinor, 1978-, et al.
(author)
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Optimal allocation to treatment groups under variance heterogeneity
- 2015
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In: Statistica sinica. - Taipei : Academica sinica. - 1017-0405 .- 1996-8507. ; 25:2, s. 537-549
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Journal article (peer-reviewed)abstract
- The problem of allocating experimental units to treatment groups when variance heterogeneity over treatment groups is present is considered. A(A)- and D-A-optimal allocations are derived for estimation of linear combinations of treatment means. Explicit expressions for the design weights are provided for the A(A)-optimal design. The minimax strategy is introduced as an approach to handle unknown variances. Efficiencies of minimax allocations are evaluated.
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| 4. |
- Ghosh, Trinetri, et al.
(author)
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Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
- 2021
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In: Statistica sinica. - Taipei : Academia Sinica, Institute of Statistical Science. - 1017-0405 .- 1996-8507. ; 31:2, s. 821-842
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Journal article (peer-reviewed)abstract
- To estimate the treatment effect in an observational study, we use a semiparametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator that combines the two. The proposed estimators retains the double robustness property, while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight.
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| 5. |
- Lönnstedt, Ingrid, et al.
(author)
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Replicated microarray data
- 2002
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In: Statistica sinica. - 1017-0405 .- 1996-8507. ; 12:1, s. 31-46
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Journal article (peer-reviewed)
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| 6. |
- Raimondo, M, et al.
(author)
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A peaks over threshold model for change-point detection by wavelets
- 2004
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In: Statistica Sinica. - 1017-0405. ; 14:2, s. 395-412
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Journal article (peer-reviewed)abstract
- Newly available wavelet bases on multi-resolution analysis have exciting implications for detection of change-points. By checking the absolute value of wavelet coefficients one call detect discontinuities in ail otherwise smooth curve even in the presence of additive noise. In this paper, we combine wavelet methods and extreme value theory to test the presence of ail arbitrary number of discontinuities in an unknown function observed with noise. Our approach is based on a Peaks Over Threshold modelling of noisy wavelet transforms. Particular features of our method include the estimation of the extreme value index in the tail of the noise distribution. The critical region of our test is, derived using a Generalised Pareto Distribution approximation to normalised sums. Asymptotic results show that our method is powerful in a wide range of medium size wavelet frequencies. We compare our test with competing approaches on simulated examples and illustrate the method on Dow-Jones data.
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| 7. |
- Vännman, Kerstin
(author)
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A unified approach to capability indices
- 1995
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In: Statistica sinica. - 1017-0405 .- 1996-8507. ; 5:2, s. 805-820
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Journal article (peer-reviewed)abstract
- A new class of capability indices, containing Csbp, Csbpk, Csbpm, and Csbpmk, is defined. By varying the parameters of the studied class, indices with different properties can be found. Two estimators of the indices are considered and, assuming that the studied characteristic of the process is normally distributed and that the target value is equal to the midpoint of a two-sided specification interval, their expected values, variances, and mean square errors are derived. It is shown that studying the properties of the class of indices alone, without taking the properties of its estimators into account, might be misleading.
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| 8. |
- Wiedenbeck, M., et al.
(author)
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Changing Parameters by Partial Mappings
- 2010
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In: Statistica Sinica. - 1017-0405. ; 20:2, s. 823-836
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Journal article (peer-reviewed)abstract
- Changes between different sets of parameters are often needed in multivariate statistical modeling, such as transformations within linear regression or in exponential models. There may, for instance, be specific inference questions based on subject matter interpretations, alternative well-fitting constrained models, compatibility judgements of seemingly distinct constrained models, or different reference priors under alternative parameterizations. We introduce and discuss a partial mapping, called partial replication, and relate it to a more complex mapping, called partial inversion. Both operations are used to decompose matrix operations, to explain recursion relations among sets of linear parameters, to change between different types of linear models, to approximate maximum-likelihood estimates in exponential family models under independence constraints, and to switch partially between sets of canonical and moment parameters in exponential family distributions or between sets of corresponding maximum-likelihood estimates.
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| 9. |
- Yue, Yu (Ryan), et al.
(author)
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Bayesian Generalized Two-way ANOVA Modeling for Functional Data Using INLA
- 2019
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In: Statistica Sinica. - : Statistica Sinica (Institute of Statistical Science). - 1017-0405. ; 29:2, s. 741-767
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Journal article (peer-reviewed)abstract
- Functional analysis of variance (ANOVA) modeling has been proved particularly useful to investigate the dynamic changes of functional data according to certain categorical factors and their interactions. The current existing methods often encounter difficulties when the functional data are high-dimensional, non-Gaussian, and/or exhibit certain shape characteristics that vary with spatial locations. In this paper, we investigate the functional two-way ANOVA models from a novel Bayesian perspective. A class of highly flexible Gaussian Markov random fields (GMRF) are taken as priors on the functions in the model, which allows us to model various types of functional effects, such as (discrete or continuous) temporal effects and (point-level or areal) spatial effects. The resulting posterior distributions are obtained by an efficient computational tool based on integrated nested Laplace approximations (INLA) Rue, Martino and Chopin (2009). We then employ the excursion method introduced by Bolin and Lindgren (2015) to build simultaneous credible intervals of functional effects and test their significance from a Bayesian point of view. A simulation study and multiple data examples are presented to demonstrate the merits of our method.
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