Miracle-dz/UniFAR-FTUs huggingface dataset:
A brief history of the Fail Safe Number in Applied Research

Rosenthal's (1979) Fail-Safe-Number (FSN) is probably one of the best known statistics in the context of meta-analysis aimed to estimate the number of unpublished studies in meta-analyses required to bring the meta-analytic mean effect size down to a statistically insignificant level. Already before Scargle's (2000) and Schonemann & Scargle's (2008) fundamental critique on the claimed stability of the basic rationale of the FSN approach, objections focusing on the basic assumption of the FSN which treats the number of studies as unbiased with averaging null were expressed throughout the history of the FSN by different authors (Elashoff, 1978; Iyengar & Greenhouse, 1988a; 1988b; see also Scargle, 2000). In particular, Elashoff's objection appears to be important because it was the very first critique pointing directly to the central problem of the FSN: "R & R claim that the number of studies hidden in the drawers would have to be 65,000 to achieve a mean effect size of zero when combined with the 345 studies reviewed here. But surely, if we allowed the hidden studies to be negative, on the average no more than 345 hidden studies would be necessary to obtain a zero mean effect size" (p. 392). Thus, users of meta-analysis could have been aware right from the beginning that something was wrong with the statistical reasoning of the FSN. In particular, from an applied research perspective, it is therefore of interest whether any of the fundamental objections on the FSN are reflected in standard handbooks on meta-analysis as well as -and of course even more importantly- in meta-analytic studies itself.
Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption

In the information system research, a question of particular interest is to interpret and to predict the probability of a firm to adopt a new technology such that market promotions are targeted to only those firms that were more likely to adopt the technology. Typically, there exists significant difference between the observed number of ``adopters'' and ``nonadopters,'' which is usually coded as binary response. A critical issue involved in modeling such binary response data is the appropriate choice of link functions in a regression model. In this paper we introduce a new flexible skewed link function for modeling binary response data based on the generalized extreme value (GEV) distribution. We show how the proposed GEV links provide more flexible and improved skewed link regression models than the existing skewed links, especially when dealing with imbalance between the observed number of 0's and 1's in a data. The flexibility of the proposed model is illustrated through simulated data sets and a billing data set of the electronic payments system adoption from a Fortune 100 company in 2005.
Universality of covariance matrices

In this paper we prove the universality of covariance matrices of the form $H_{N\times N}={X}^{\dagger}X$ where $X$ is an ${M\times N}$ rectangular matrix with independent real valued entries $x_{ij}$ satisfying $\mathbb{E}x_{ij}=0$ and $\mathbb{E}x^2_{ij}={\frac{1}{M}}$, $N$, $M\to \infty$. Furthermore it is assumed that these entries have sub-exponential tails or sufficiently high number of moments. We will study the asymptotics in the regime $N/M=d_N\in(0,\infty),\lim_{N\to\infty}d_N\neq0,\infty$. Our main result is the edge universality of the sample covariance matrix at both edges of the spectrum. In the case $\lim_{N\to\infty}d_N=1$, we only focus on the largest eigenvalue. Our proof is based on a novel version of the Green function comparison theorem for data matrices with dependent entries. En route to proving edge universality, we establish that the Stieltjes transform of the empirical eigenvalue distribution of $H$ is given by the Marcenko-Pastur law uniformly up to the edges of the spectrum with an error of order $(N\eta)^{-1}$ where $\eta$ is the imaginary part of the spectral parameter in the Stieltjes transform. Combining these results with existing techniques we also show bulk universality of covariance matrices. All our results hold for both real and complex valued entries.
Semigroups and sequential importance sampling for multiway tables and beyond

When an interval of integers between the lower bound l_i and the upper bounds u_i is the support of the marginal distribution n_i|(n_{i-1}, .,n_1), Chen et al. 2005 noticed that sampling from the interval at each step, for n_i during the sequential importance sampling (SIS) procedure, always produces a table which satisfies the marginal constraints. However, in general, the interval may not be equal to the support of the marginal distribution. In this case, the SIS procedure may produce tables which do not satisfy the marginal constraints, leading to rejection [Chen et al. 2006]. Rejecting tables is computationally expensive and incorrect proposal distributions result in biased estimators for the number of tables given its marginal sums. This paper has two focuses; (1) we propose a correction coefficient which corrects an interval of integers between the lower bound l_i and the upper bounds u_i to the support of the marginal distribution asymptotically even with rejections and with the same time complexity as the original SIS procedure (2) using univariate and bivariate logistic regression models, we present extensive experiments on simulated data sets for estimating the number of tables, and (3) we applied the volume test proposed by Diaconis and Efron 1985 on 2x2x6 randomly generated tables to compare the performance of SIS versus MCMC. When estimating the number of tables in our simulation study, we used univariate and bivariate logistic regression models since under these models the SIS procedure seems to have higher rate of rejections even with small tables. We also apply our correction coefficients to data sets on coronary heart disease and occurrence of esophageal cancer.
Estimation for Latent Factor Models for High-Dimensional Time Series

This paper deals with the dimension reduction for high-dimensional time series based on common factors. In particular we allow the dimension of time series p to be as large as, or even larger than, the sample size n. The estimation for the factor loading matrix and the factor process itself is carried out via an eigenanalysis for a pp non-negative definite matrix. We show that when all the factors are strong in the sense that the norm of each column in the factor loading matrix is of the order p 1=2 , the estimator for the factor loading matrix, as well as the resulting estimator for the precision matrix of the original p-variant time series, are weakly consistent in L2-norm with the convergence rates independent of p. This result exhibits clearly that the 'curse' is canceled out by the 'blessings' in dimensionality. We also establish the asymptotic properties of the estimation when not all factors are strong. For the latter case, a two-step estimation procedure is preferred accordingly to the asymptotic theory. The proposed methods together with their asymptotic properties are further illustrated in a simulation study. An application to a real data set is also reported.
Further properties of Gaussian Reproducing Kernel Hilbert Spaces

We generalize the orthonormal basis for the Gaussian RKHS described in \cite{MinhGaussian2010} to an infinite, continuously parametrized, family of orthonormal bases, along with some implications. The proofs are direct generalizations of those in \cite{MinhGaussian2010}.
Overlapping stochastic block models with application to the French political blogosphere

Complex systems in nature and in society are often represented as networks, describing the rich set of interactions between objects of interest. Many deterministic and probabilistic clustering methods have been developed to analyze such structures. Given a network, almost all of them partition the vertices into disjoint clusters, according to their connection profile. However, recent studies have shown that these techniques were too restrictive and that most of the existing networks contained overlapping clusters. To tackle this issue, we present in this paper the Overlapping Stochastic Block Model. Our approach allows the vertices to belong to multiple clusters, and, to some extent, generalizes the well-known Stochastic Block Model [Nowicki and Snijders (2001)]. We show that the model is generically identifiable within classes of equivalence and we propose an approximate inference procedure, based on global and local variational techniques. Using toy data sets as well as the French Political Blogosphere network and the transcriptional network of Saccharomyces cerevisiae, we compare our work with other approaches.
A Threshold Regularization Method for Inverse Problems

A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. However, these so-called filter methods are generally restricted to monotonic transformations, e.g. the Tikhonov regularization or the spectral cut-off. In this paper, we point out that in several cases, non-monotonic sequences of filters are more efficient. We study a regularization method that naturally extends the spectral cut-off procedure to nonmonotonic sequences and provide several oracle inequalities, showing the method to be nearly optimal under mild assumptions. Then, we extend the method to inverse problems with noisy operator and provide efficiency results i n a newly introduced conditional framework.
Using epidemic prevalence data to jointly estimate reproduction and removal

This study proposes a nonhomogeneous birth-death model which captures the dynamics of a directly transmitted infectious disease. Our model accounts for an important aspect of observed epidemic data in which only symptomatic infecteds are observed. The nonhomogeneous birth-death process depends on survival distributions of reproduction and removal, which jointly yield an estimate of the effective reproduction number R(t) as a function of epidemic time. We employ the Burr distribution family for the survival functions and, as special cases, proportional rate and accelerated event-time models are also employed for the parameter estimation procedure. As an example, our model is applied to an outbreak of avian influenza (H7N7) in the Netherlands, 2003, confirming that the conditional estimate of R(t) declined below unity for the first time on day 23 since the detection of the index case.
Analog Sparse Approximation with Applications to Compressed Sensing

Recent research has shown that performance in signal processing tasks can often be significantly improved by using signal models based on sparse representations, where a signal is approximated using a small number of elements from a fixed dictionary. Unfortunately, inference in this model involves solving non-smooth optimization problems that are computationally expensive. While significant efforts have focused on developing digital algorithms specifically for this problem, these algorithms are inappropriate for many applications because of the time and power requirements necessary to solve large optimization problems. Based on recent work in computational neuroscience, we explore the potential advantages of continuous time dynamical systems for solving sparse approximation problems if they were implemented in analog VLSI. Specifically, in the simulated task of recovering synthetic and MRI data acquired via compressive sensing techniques, we show that these systems can potentially perform recovery at time scales of 10-20{\mu}s, supporting datarates of 50-100 kHz (orders of magnitude faster that digital algorithms). Furthermore, we show analytically that a wide range of sparse approximation problems can be solved in the same basic architecture, including approximate $\ell^p$ norms, modified $\ell^1$ norms, re-weighted $\ell^1$ and $\ell^2$, the block $\ell^1$ norm and classic Tikhonov regularization.
Smoothness of Gaussian Conditional Independence Models

Conditional independence in a multivariate normal (or Gaussian) distribution is characterized by the vanishing of subdeterminants of the distri- bution's covariance matrix. Gaussian conditional independence models thus correspond to algebraic subsets of the cone of positive definite matrices. For statistical inference in such models it is important to know whether or not the model contains singularities. We study this issue in models involving up to four random variables. In particular, we give examples of conditional independence relations which, despite being probabilistically representable, yield models that non-trivially decompose into a finite union of several smooth submodels.
Multiple Hypotheses Testing For Variable Selection

Many methods have been developed to estimate the set of relevant variables in a sparse linear model Y= XB+e where the dimension p of B can be much higher than the length n of Y. Here we propose two new methods based on multiple hypotheses testing, either for ordered or non-ordered variables. Our procedures are inspired by the testing procedure proposed by Baraud et al (2003). The new procedures are proved to be powerful under some conditions on the signal and their properties are non asymptotic. They gave better results in estimating the set of relevant variables than both the False Discovery Rate (FDR) and the Lasso, both in the common case (p n).
Handling Covariates in the Design of Clinical Trials.

There has been a split in the statistics community about the need for taking covariates into account in the design phase of a clinical trial. There are many advocates of using stratification and covariate-adaptive random ization to promote balance on certain known covariates. However, balance does not always promote efficiency or ensure more patients are assigned to the better treatment. We describe these procedures, including model-based procedures, for incorporating covariates into the design of clinical trials, and give examples where balance, efficiency and ethical considerations may be in conflict. We advocate a new class of procedures, covariate-adjusted response adaptive (CARA) randomization procedures that attempt to optimize both efficiency and ethical considerations, while maintaining randomization. We review all these procedures, present a few new simulation studies, and con clude with our philosophy.
Mirror bootstrap method for testing hypotheses of one mean

The general philosophy for bootstrap or permutation methods for testing hypotheses is to simulate the variation of the test statistic by generating the sampling distribution which assumes both that the null hypothesis is true, and that the data in the sample is somehow representative of the population. This philosophy is inapplicable for testing hypotheses for a single parameter like the population mean, since the two assumptions are contradictory (e.g., how can we assume both that the mean of the population is $\mu_0,$ and that the individuals in the sample with a mean $M \ne \mu_0$ are representative of the population? ). The Mirror Bootstrap resolves that conundrum. The philosophy of the Mirror Bootstrap method for testing hypotheses regarding one population parameter is that we assume both that the null hypothesis is true, and that the individuals in our sample are as representative as they could be without assuming more extreme cases than observed. For example, the Mirror Bootstrap method for testing hypotheses of one mean uses a generated symmetric distribution constructed by reflecting the original sample around the hypothesized population mean $\mu_0$. Simulations of the performance of the Mirror Bootstrap for testing hypotheses of one mean show that, while the method is slightly on the conservative side for very small samples, its validity and power quickly approach that of the widely used t-test. The philosophy of the Mirror Bootstrap is sufficiently general to be adapted for testing hypotheses about other parameters; this exploration is left for future research.
A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It is based on a temporal autoregressive convolution with spatially colored and temporally white innovations. By linking the advection parameter of the convolution kernel to an external wind vector, the model is temporally nonstationary. Further, it allows for nonseparable and anisotropic covariance structures. With the help of the Voronoi tessellation, we construct a natural parametrization, that is, space as well as time resolution consistent, for data lying on irregular grid points. In the application, the statistical model combines forecasts of three other meteorological variables obtained from a numerical weather prediction model with past precipitation observations. The model is then used to predict three-hourly precipitation over 24 hours. It performs better than a separable, stationary and isotropic version, and it performs comparably to a deterministic numerical weather prediction model for precipitation and has the advantage that it quantifies prediction uncertainty.
Estimating Random Effects via Adjustment for Density Maximization

We develop and evaluate point and interval estimates for the random effects $\theta_i$, having made observations $y_i|\theta_i\stackrel{\m athit{ind}}{\sim}N[\theta_i,V_i],i=1,. ,k$ that follow a two-level Normal hierarchical model. Fitting this model requires assessing the Level-2 variance $A\equiv\operatorname {Var}(\theta_i)$ to estimate shrinkages $B_i\equiv V_i/(V_i+A)$ toward a (possibly estimated) subspace, with $B_i$ as the target because the conditional means and variances of $\theta_i$ depend linearly on $B_i$, not on $A$. Adjustment for density maximization, ADM, can do the fitting for any smooth prior on $A$. Like the MLE, ADM bases inferences on two derivatives, but ADM can approximate with any Pearson family, with Beta distributions being appropriate because shrinkage factors satisfy $0\le B_i\le1$. Our emphasis is on frequency properties, which leads to adopting a uniform prior on $A\ge0$, which then puts Stein's harmonic prior (SHP) on the $k$ random effects. It is known for the "equal variances case" $V_1=. =V_k$ that formal Bayes procedures for this prior produce admissible minimax estimates of the random effects, and that the posterior variances are large enough to provide confidence intervals that meet their nominal coverages. Similar results are seen to hold for our approximating "ADM-SHP" procedure for equal variances and also for the unequal variances situations checked here. For shrinkage coefficient estimation, the ADM-SHP procedure allows an alternative frequency interpretation. Writing $L(A)$ as the likelihood of $B_i$ with $i$ fixed, ADM-SHP estimates $B_i$ as $\hat{B_i}=V_i/(V_i+\hat{A})$ with $\hat{A}\equiv \operatorname {argmax}(A*L(A))$. This justifies the term "adjustment for likelihood maximization," ALM.
A consistent multivariate test of association based on ranks of distances

We consider the problem of detecting associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and consistent against all alternatives. The test has a simple form, is easy to implement, and has good power. Copyright 2013, Oxford University Press.
Learning loosely connected Markov random fields

We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C log p, where p is the size of the graph and the constant C depends on the parameters of the model. We show that several previously studied models are examples of loosely connected Markov random fields, and our algorithm achieves the same or lower computational complexity than the previously designed algorithms for individual cases. We also get new results for more general graphical models, in particular, our algorithm learns general Ising models on the Erdős-Renyi random graph 𝒢(p,cp) correctly with running time O(np5).
General Construction of Irreversible Kernel in Markov Chain Monte Carlo

The Markov chain Monte Carlo update method to construct an irreversible kernel has been reviewed and extended to general state spaces. The several convergence conditions of the Markov chain were discussed. The alternative methods to the Gibbs sampler and the Metropolis-Hastings algorithm were proposed and assessed in some models. The distribution convergence and the sampling efficiency are significantly improved in the Potts model, the bivariate Gaussian model, and so on. This approach using the irreversible kernel can be applied to any Markov chain Monte Carlo sampling and it is expected to improve the efficiency in general.
Kernels and Ensembles

Since their emergence in the 1990s, the support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of two broad categories: kernel methods and ensemble methods. In this expository article, I discuss the main ideas behind these two types of methods, namely how to transform linear algorithms into nonlinear ones by using kernel functions, and how to make predictions with an ensemble or a collection of models rather than a single model. I also share my personal perspectives on how these ideas have influenced and shaped my own research. In particular, I present two recent algorithms that I have invented with my collaborators: LAGO, a fast kernel algorithm for unbalanced classification and rare target detection; and Darwinian evolution in parallel universes, an ensemble method for variable selection.
On penalized estimation for dynamical systems with small noise

We consider a dynamical system with small noise for which the drift is parametrized by a finite dimensional parameter. For this model we consider minimum distance estimation from continuous time observations under $l^p$-penalty imposed on the parameters in the spirit of the Lasso approach with the aim of simultaneous estimation and model selection. We study the consistency and the asymptotic distribution of these Lasso-type estimators for different values of $p$. For $p=1$ we also consider the adaptive version of the Lasso estimator and establish its oracle properties.