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Does "model-free" forecasting really outperform the "true" model? A reply to Perretti et al
"model-free" forecasting outperform "true" model
2013/6/14
Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state-space solutions to this problem may provide biased pa...
Evolutionary Model of the Growth and Size of Firms
firm size distribution firm growth Gibrat's law product growth size-variance relationship growth rate distribution, Subbotin distribution Laplace distribution Pareto distribution price distribution human activity evolutionary economics product life cycle learning curve market size Hendersons law law of diminishing returns competitive markets
2012/9/18
The key idea of this model is that firms are the result of an evolutionary process. Based on demand and supply considerations the evolutionary model presented here derives explic...
Bayesian Analysis of Multiway Tables in Association Studies: A Model Comparison Approach
Bayesian Analysis Multiway Tables Association Studies Model Comparison Approach
2012/9/17
We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coecients in comp...
A non-parametric mixture model for topic modeling over time
non-parametric mixture model topic modeling over time
2012/9/17
A single, stationary topic model such as la-tent Dirichlet allocation is inappropriate for modeling corpora that span long time peri-ods, as the popularity of topics is likely to change over time. A n...
Simultaneous Model Selection and Estimation for Mean and Association Structures with Clustered Binary Data
association clustered binary data generalized estimating equation logistic regression variable selection
2012/9/17
This paper investigates the property of the penalized estimating equations when both the mean and association structures are modelled. To select variables for the mean and association structures seque...
The Dependence of Routine Bayesian Model Selection Methods on Irrelevant Alternatives
Bayesian Model Selection Methods Alternatives
2012/9/17
Bayesian methods - either based on Bayes Factors or BIC - are now widely used for model selection. One property that might reasonably be demanded of any model
selection method is that if a modelM1 is...
Prediction and Computer Model Calibration Using Outputs From Multi-fidelity Simulators
Computer Experiment Gaussian process Markov Chain Monte Carlo.
2012/9/17
Computer codes are widely used to describe physical processes in lieu of physical observations.In some cases, more than one computer simulator, each with di erent degrees of delity, can be used to ex...
Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression
Nonparametric hypotheses testing sharp asymptotics separation rates minimax approach high-dimensional regression.
2012/9/17
We consider the problem of testing a particular type of composite null hypothesis under a nonparametric multivariate regression model. For a given quadraticfunctional Q, the null hypothesis states tha...
Inter-Coder Agreement for Nominal Scales: A Model-based Approach
Inter-Coder Agreement Nominal Scales Model-based Approach
2012/9/17
Inter-coder agreement measures, like Cohen’sκ, correct the relative frequency of agreement between coders to account for agreement which simply occurs by
chance. However, in some situations these mea...
Massive parallelization of serial inference algorithms for a complex generalized linear model
Massive parallelization serial inference algorithms generalized linear model
2012/9/17
Following a series of high-pro le drug safety disasters in recent years, many countries are redoubling their e orts to ensure the safety of licensed medical products. Large-scale observa-tional databa...
Oracle inequalities for computationally adaptive model selection
Oracle computationally adaptive model selection
2012/9/17
We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspe...
Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies
Efficient computation a linear mixed model on large-scale data sets applications genetic studies
2012/9/19
Motivated by genome-wide association studies we consider astan-dard linear model with one additional random effect in situations where many predictors have been collected on the same subjects and each...
Adaptive confidence bands in the nonparametric fixed design regression model
Adaptive confidence bands nonparametric fixed design regression model
2012/9/19
In this note, we consider the problem of existence of adaptive confidence bands in the fixed design regression model, adapting ideas in Hoffmann and Nickl [10] to the present case. In the course of th...
Gaussian Oracle Inequalities for Structured Selection in Non-Parametric Cox Model
Gaussian Oracle Inequalities Structured Selection Non-Parametric Cox Model
2012/9/19
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard乫s model. Due to high impact of sequencing data, carrying genet...
A Simple Probabilistic and Point-process Response Model for Predicting Every Spike in Optogenetics
optogenetics point processes generalized linear models response functions neuronal data generalized additive models prediction.
2012/9/19
Optogenetics is a new tool to stimulate genetically targeted neuronal circuits us-ing light flashes that can be delivered at high frequencies. It has shown promise for studying neural circuits that ar...