# Statistics

## New submissions

[ total of 35 entries: 1-35 ]
[ showing up to 2000 entries per page: fewer | more ]

### New submissions for Fri, 20 Oct 17

[1]
Title: Diagnosing missing always at random in multivariate data
Subjects: Applications (stat.AP); Methodology (stat.ME)

Models for analyzing multivariate data sets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable - a twofold assumption dependent on the mode of inference. The first part, which is the focus here, under the Bayesian and direct likelihood paradigms, requires that the missing data are missing at random (MAR); in contrast, the frequentist-likelihood paradigm demands that the missing data mechanism always produces MAR data, a condition known as missing always at random (MAAR). Under certain regularity conditions, assuming MAAR leads to an assumption that can be tested using the observed data alone namely, the missing data indicators only depend on fully observed variables. Here, we propose three different diagnostics procedures that not only indicate when this assumption is invalid but also suggest which variables are the most likely culprits. Although MAAR is not a necessary condition to ensure validity under the Bayesian and direct likelihood paradigms, it is sufficient, and evidence for its violation should encourage the statistician to conduct a targeted sensitivity analysis.

[2]
Title: Edgeworth correction for the largest eigenvalue in a spiked PCA model
Subjects: Statistics Theory (math.ST)

We study improved approximations to the distribution of the largest eigenvalue $\hat{\ell}$ of the sample covariance matrix of $n$ zero-mean Gaussian observations in dimension $p+1$. We assume that one population principal component has variance $\ell > 1$ and the remaining noise' components have common variance $1$. In the high dimensional limit $p/n \to \gamma > 0$, we begin study of Edgeworth corrections to the limiting Gaussian distribution of $\hat{\ell}$ in the supercritical case $\ell > 1 + \sqrt \gamma$. The skewness correction involves a quadratic polynomial as in classical settings, but the coefficients reflect the high dimensional structure. The methods involve Edgeworth expansions for sums of independent non-identically distributed variates obtained by conditioning on the sample noise eigenvalues, and limiting bulk properties \textit{and} fluctuations of these noise eigenvalues.

[3]
Title: A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series
Comments: 17 pages, 8 figures, 2 tables
Subjects: Methodology (stat.ME); Learning (cs.LG); Machine Learning (stat.ML)

This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of $N$ time series, the Bayesian model first partitions them into independent clusters using a Chinese restaurant process prior. Within a cluster, all time series are modeled jointly using a novel "temporally-coupled" extension of the Chinese restaurant process mixture. Markov chain Monte Carlo techniques are used to obtain samples from the posterior distribution, which are then used to form predictive inferences. We apply the technique to challenging prediction and imputation tasks using seasonal flu data from the US Center for Disease Control and Prevention, demonstrating competitive imputation performance and improved forecasting accuracy as compared to several state-of-the art baselines. We also show that the model discovers interpretable clusters in datasets with hundreds of time series using macroeconomic data from the Gapminder Foundation.

[4]
Title: Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
Subjects: Machine Learning (stat.ML); Learning (cs.LG); Optimization and Control (math.OC)

The past decade has witnessed a successful application of deep learning to solving many challenging problems in machine learning and artificial intelligence. However, the loss functions of deep neural networks (especially nonlinear networks) are still far from being well understood from a theoretical aspect. In this paper, we enrich the current understanding of the landscape of the square loss functions for three types of neural networks. Specifically, when the parameter matrices are square, we provide an explicit characterization of the global minimizers for linear networks, linear residual networks, and nonlinear networks with one hidden layer. Then, we establish two quadratic types of landscape properties for the square loss of these neural networks, i.e., the gradient dominance condition within the neighborhood of their full rank global minimizers, and the regularity condition along certain directions and within the neighborhood of their global minimizers. These two landscape properties are desirable for the optimization around the global minimizers of the loss function for these neural networks.

[5]
Title: Growth Mixture Modeling with Measurement Selection
Authors: Abby Flynt, Nema Dean
Subjects: Methodology (stat.ME)

Growth mixture models are an important tool for detecting group structure in repeated measures data. Unlike traditional clustering methods, they explicitly model the repeat measurements on observations, and the statistical framework they are based on allows for model selection methods to be used to select the number of clusters. However, the basic growth mixture model makes the assumption that all of the measurements in the data have grouping information/separate the clusters. In other clustering contexts, it has been shown that including non-clustering variables in clustering procedures can lead to poor estimation of the group structure both in terms of the number of clusters and cluster membership/parameters. In this paper, we present an extension of the growth mixture model that allows for incorporation of stepwise variable selection based on the work done by Maugis et al. (2009) and Raftery and Dean (2006). Results presented on a simulation study suggest that the method performs well in correctly selecting the clustering variables and improves on recovery of the cluster structure compared with the basic growth mixture model. The paper also presents an application of the model to a clinical study dataset and concludes with a discussion and suggestions for directions of future work in this area.

[6]
Title: Providing Accurate Models across Private Partitioned Data: Secure Maximum Likelihood Estimation
Subjects: Methodology (stat.ME)

This paper focuses on the privacy paradigm of providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We address the situation where the analysis demands data from multiple physically separate databases which cannot be combined. Motivating this problem are analyses using multiple data sources that currently are only possible through extension work creating a trusted user network. We develop and demonstrate a method for accurate calculation of the multivariate normal likelihood equation, for a set of parameters given the partitioned data, which can then be maximized to obtain estimates. These estimates are achieved without sharing any data or any true intermediate statistics of the data across firewalls. We show that under a certain set of assumptions our method for estimation across these partitions achieves identical results as estimation with the full data. Privacy is maintained by adding noise at each partition. This ensures each party receives noisy statistics, such that the noise cannot be removed until the last step to obtain a single value, the true total log-likelihood. Potential applications include all methods utilizing parameter estimation through maximizing the multivariate normal likelihood equation. We give detailed algorithms, along with available software, and both a real data example and simulations estimating structural equation models (SEMs) with partitioned data.

[7]
Title: Universal Convergence of Kriging
Subjects: Statistics Theory (math.ST)

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work we obtain some error bounds for the (simple) kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Mat\'ern correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Mat\'ern correlation functions.

[8]
Title: Importance sampling the union of rare events with an application to power systems analysis
Subjects: Computation (stat.CO); Numerical Analysis (cs.NA); Numerical Analysis (math.NA)

This paper presents a method for estimating the probability $\mu$ of a union of $J$ rare events. The method uses $n$ samples, each of which picks one of the rare events at random, samples conditionally on that rare event happening and counts the total number of rare events that happen. We call it ALORE, for at least one rare event'. The ALORE estimate is unbiased and has a coefficient of variation no larger than $\sqrt{(J+J^{-1}-2)/(4n)}$. The coefficient of variation is also no larger than $\sqrt{(\bar\mu/\mu-1)/n}$ where $\bar\mu$ is the union bound. Our motivating problem comes from power system reliability, where the phase differences between connected nodes have a joint Gaussian distribution and the $J$ rare events arise from unacceptably large phase differences. In the grid reliability problems even some events defined by $5772$ constraints in $326$ dimensions, with probability below $10^{-22}$, are estimated with a coefficient of variation of about $0.0024$ with only $n=10{,}000$ sample values.

[9]
Title: On Affine and Conjugate Nonparametric Regression
Subjects: Statistics Theory (math.ST)

If the nonparametric regression function of a response variable $Y$ on covariates $X$ and $Z$ is an affine function of $X$ such that the slope $\alpha$ and the intercept $\beta$ are real valued measurable functions on the range of the completely arbitrary random element $Z$, then, under the assumptions that $X$ has a finite moment of order greater than or equal to 2, $Y$ has a finite moment of conjugate order, and $\alpha(Z)$ and $\alpha(Z)X$ have finite first moments, we show that the nonparametric regression function equals the least squares linear regression function of $Y$ on $X$ with all the moments that appear in the expression of the linear regression function calculated conditional on $Z$.
Consequently, conditional mean independence implies zero conditional covariance and a degenerate version of the aforesaid affine form for the nonparametric regression function, whereas the aforesaid affine form and zero conditional covariance imply conditional mean independence.
That the least squares linear regression formula for the nonparametric regression function holds if $(X, Y, Z)$ is multivariate Normal is not difficult to establish without appealing to the aforesaid affine form for the nonparametric regression function; however, to show that the least squares linear regression formula holds if $X$ is Bernoulli, when $Y$ has only a finite first moment and $Z$ is completely arbitrary, it seems one must verify that the nonparametric regression function has the aforesaid affine form using that 1 is the conjugate exponent of $\infty$, since a direct, tedious verification of the formula is possible if $Y$ is bounded, or if $Y$ has a finite second moment and the range of $Z$ is a Polish space, but seems impossible otherwise.

[10]
Title: On the Relationship between Conditional (CAR) and Simultaneous (SAR) Autoregressive Models
Comments: 18 pages, 4 figures
Subjects: Statistics Theory (math.ST)

We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive models. We review the literature on this topic and find that it is mostly incomplete. Our main result is that a SAR model can be written as a unique CAR model, and while a CAR model can be written as a SAR model, it is not unique. In fact, we show how any multivariate Gaussian distribution on a finite set of points with a positive-definite covariance matrix can be written as either a CAR or a SAR model. We illustrate how to obtain any number of SAR covariance matrices from a single CAR covariance matrix by using Givens rotation matrices on a simulated example. We also discuss sparseness in the original CAR construction, and for the resulting SAR weights matrix. For a real example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show that a geostatistical model optimizes the likelihood much better than typical first-order CAR models. We then use the implied weights from the geostatistical model to estimate CAR model parameters that provides the best overall optimization.

[11]
Title: Modal Regression using Kernel Density Estimation: a Review
Authors: Yen-Chi Chen
Comments: 23 pages, 2 figures; a short invited review paper
Subjects: Methodology (stat.ME); Econometrics (econ.EM)

We review recent advances in modal regression studies using kernel density estimation. Modal regression is an alternative approach for investigating relationship between a response variable and its covariates. Specifically, modal regression summarizes the interactions between the response variable and covariates using the conditional mode or local modes. We first describe the underlying model of modal regression and its estimators based on kernel density estimation. We then review the asymptotic properties of the estimators and strategies for choosing the smoothing bandwidth. We also discuss useful algorithms and similar alternative approaches for modal regression, and propose future direction in this field.

[12]
Title: Minimax Estimation of Bandable Precision Matrices
Subjects: Machine Learning (stat.ML)

The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is assumed to be multivariate normal, the sparsity pattern in the inverse covariance matrix, commonly referred to as the precision matrix, corresponds to the adjacency matrix representation of the Gauss-Markov graph, which encodes conditional independence statements between variables. Minimax results under the spectral norm have previously been established for covariance matrices, both sparse and banded, and for sparse precision matrices. We establish minimax estimation bounds for estimating banded precision matrices under the spectral norm. Our results greatly improve upon the existing bounds; in particular, we find that the minimax rate for estimating banded precision matrices matches that of estimating banded covariance matrices. The key insight in our analysis is that we are able to obtain barely-noisy estimates of $k \times k$ subblocks of the precision matrix by inverting slightly wider blocks of the empirical covariance matrix along the diagonal. Our theoretical results are complemented by experiments demonstrating the sharpness of our bounds.

[13]
Title: Causal inference for binary non-independent outcomes
Subjects: Methodology (stat.ME)

Causal inference on multiple non-independent outcomes raises serious challenges, because multivariate techniques that properly account for the outcome's dependence structure need to be considered. We focus on the case of binary outcomes framing our discussion in the potential outcome approach to causal inference. We define causal effects of treatment on joint outcomes introducing the notion of product outcomes. We also discuss a decomposition of the causal effect on product outcomes into intrinsic and extrinsic causal effects, which respectively provide information on treatment effect on the intrinsic (product) structure of the product outcomes and on the outcomes' dependence structure. We propose a log-mean linear regression approach for modeling the distribution of the potential outcomes, which is particularly appealing because all the causal estimands of interest and the decomposition into intrinsic and extrinsic causal effects can be easily derived by model parameters. The method is illustrated in two randomized experiments concerning (i) the effect of the administration of oral pre-surgery morphine on pain intensity after surgery; and (ii) the effect of honey on nocturnal cough and sleep difficulty associated with childhood upper respiratory tract infections.

[14]
Title: Reti bayesiane per lo studio del fenomeno degli incidenti stradali tra i giovani in Toscana
Subjects: Applications (stat.AP); Machine Learning (stat.ML)

This paper aims to analyse adolescents' road accidents in Tuscany. The analysis is based on the Database Edit of Osservatorio di Epidemiologia della Toscana. Complexity and heterogeneity of Edit's data represet an interesting scope to apply Machine Learning methods. In particular, in this paper is proposed an analysis based on a Bayesian probabilistic network, used to discover relationships between adolescents' characteristics and behaviours that are more often associated with an audacious driving style. The probabilistic network developed by this study can be considered a useful starting point for follow up reasearches, aiming to develop a causal network, a tool to limit this phenomenon.

[15]
Title: Binary Classification from Positive-Confidence Data
Subjects: Machine Learning (stat.ML); Learning (cs.LG)

Reducing labeling costs in supervised learning is a critical issue in many practical machine learning applications. In this paper, we consider positive-confidence (Pconf) classification, the problem of training a binary classifier only from positive data equipped with confidence. Pconf classification can be regarded as a discriminative extension of one-class classification (which is aimed at "describing" the positive class), with ability to tune hyper-parameters for "classifying" positive and negative samples. Pconf classification is also related to positive-unlabeled (PU) classification (which uses hard-labeled positive data and unlabeled data), allowing us to avoid estimating the class priors, which is a critical bottleneck in typical PU classification methods. For the Pconf classification problem, we provide a simple empirical risk minimization framework and give a formulation for linear-in-parameter models that can be implemented easily and computationally efficiently. We also theoretically establish the consistency and generalization error bounds for Pconf classification, and demonstrate the practical usefulness of the proposed method through experiments.

[16]
Title: Comparison of statistical procedures for Gaussian graphical model selection
Authors: Ivan Grechikhin
Subjects: Methodology (stat.ME)

Graphical models are used in a variety of problems to uncover hidden structures. There is a huge number of different identification procedures, constructed for different purposes. However, it is important to research different properties of such procedures and compare them in order to find out the best procedure or the best use case for some specific procedure. In this paper, some statistical identification procedures are compared using different measures, such as Type I and Type II errors, ROC AUC.

[17]
Title: Environmental contours based on kernel density estimation
Comments: version that the authors' prepared for the proceedings of DEWEK 2017, 4 pages, 3 figures
Subjects: Methodology (stat.ME)

An offshore wind turbine needs to withstand the environmental loads, which can be expected during its life time. Consequently, designers must define loads based on extreme environmental conditions to verify structural integrity. The environmental contour method is an approach to systematically derive these extreme environmental design conditions. The method needs a probability density function as its input. Here we propose the use of constant bandwidth kernel density estimation to derive the joint probability density function of significant wave height and wind speed. We compare kernel density estimation with the currently recommended conditional modeling approach. In comparison, kernel density estimation seems better suited to describe the statistics of environmental conditions of simultaneously high significant wave height and wind speed. Consequently, an environmental contour based on kernel density estimation does include these environmental conditions while an environmental contour based on the conditional modeling approach does not. Since these environmental conditions often lead to the highest structural responses, it is especially important that the used method outputs these conditions as design requirements.

[18]
Title: LSMM: A statistical approach to integrating functional annotations with genome-wide association studies
Subjects: Methodology (stat.ME); Genomics (q-bio.GN)

Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in the non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e., a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remains unknown. The availability of functional annotation data enables us to address the above challenges. In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase statistical power of the identification of risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWAS of complex phenotypes integrated with 9 genic category annotations and 127 tissue-specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power over conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes.

### Cross-lists for Fri, 20 Oct 17

[19]  arXiv:1710.06940 (cross-list from cs.LG) [pdf, other]
Title: Concept Drift Learning with Alternating Learners
Journal-ref: Y. Xu, R. Xu, W. Yan and P. Ardis, "Concept drift learning with alternating learners," 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 2104-2111
Subjects: Learning (cs.LG); Machine Learning (stat.ML)

Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in practical applications. A simple dual-learner ensemble strategy, alternating learners framework, is proposed. A long-memory model learns stable concepts from a long relevant time window, while a short-memory model learns transient concepts from a small recent window. The difference in prediction performance of these two models is monitored and induces an alternating policy to select, update and reset the two models. The method features an online updating mechanism to maintain the ensemble accuracy, and a concept-dependent trigger to focus on relevant data. Through empirical studies the method demonstrates effective tracking and prediction when the steaming data carry abrupt and/or gradual changes.

[20]  arXiv:1710.06952 (cross-list from math.OC) [pdf, other]
Title: Asynchronous Decentralized Parallel Stochastic Gradient Descent
Subjects: Optimization and Control (math.OC); Learning (cs.LG); Machine Learning (stat.ML)

Recent work shows that decentralized parallel stochastic gradient decent (D-PSGD) can outperform its centralized counterpart both theoretically and practically. While asynchronous parallelism is a powerful technology to improve the efficiency of parallelism in distributed machine learning platforms and has been widely used in many popular machine learning softwares and solvers based on centralized parallel protocols such as Tensorflow, it still remains unclear how to apply the asynchronous parallelism to improve the efficiency of decentralized parallel algorithms. This paper proposes an asynchronous decentralize parallel stochastic gradient descent algorithm to apply the asynchronous parallelism technology to decentralized algorithms. Our theoretical analysis provides the convergence rate or equivalently the computational complexity, which is consistent with many special cases and indicates we can achieve nice linear speedup when we increase the number of nodes or the batchsize. Extensive experiments in deep learning validate the proposed algorithm.

[21]  arXiv:1710.07110 (cross-list from cs.LG) [pdf, other]
Title: Meta-Learning via Feature-Label Memory Network
Comments: this https URL
Subjects: Learning (cs.LG); Machine Learning (stat.ML)

Deep learning typically requires training a very capable architecture using large datasets. However, many important learning problems demand an ability to draw valid inferences from small size datasets, and such problems pose a particular challenge for deep learning. In this regard, various researches on "meta-learning" are being actively conducted. Recent work has suggested a Memory Augmented Neural Network (MANN) for meta-learning. MANN is an implementation of a Neural Turing Machine (NTM) with the ability to rapidly assimilate new data in its memory, and use this data to make accurate predictions. In models such as MANN, the input data samples and their appropriate labels from previous step are bound together in the same memory locations. This often leads to memory interference when performing a task as these models have to retrieve a feature of an input from a certain memory location and read only the label information bound to that location. In this paper, we tried to address this issue by presenting a more robust MANN. We revisited the idea of meta-learning and proposed a new memory augmented neural network by explicitly splitting the external memory into feature and label memories. The feature memory is used to store the features of input data samples and the label memory stores their labels. Hence, when predicting the label of a given input, our model uses its feature memory unit as a reference to extract the stored feature of the input, and based on that feature, it retrieves the label information of the input from the label memory unit. In order for the network to function in this framework, a new memory-writingmodule to encode label information into the label memory in accordance with the meta-learning task structure is designed. Here, we demonstrate that our model outperforms MANN by a large margin in supervised one-shot classification tasks using Omniglot and MNIST datasets.

[22]  arXiv:1710.07175 (cross-list from math.CO) [pdf, ps, other]
Title: The Geometry of Gaussoids
Comments: 32 pages, 4 figures
Subjects: Combinatorics (math.CO); Commutative Algebra (math.AC); Statistics Theory (math.ST)

A gaussoid is a combinatorial structure that encodes independence in probability and statistics, just like matroids encode independence in linear algebra. The gaussoid axioms of Lnenicka and Mat\'us are equivalent to compatibility with certain quadratic relations among principal and almost-principal minors of a symmetric matrix. We develop the geometric theory of gaussoids, based on the Lagrangian Grassmannian and its symmetries. We introduce oriented gaussoids and valuated gaussoids, thus connecting to real and tropical geometry. We classify small realizable and non-realizable gaussoids. Positive gaussoids are as nice as positroids: they are all realizable via graphical models.

[23]  arXiv:1710.07186 (cross-list from math.OC) [pdf, other]
Title: A Universal Simulation Platform for Flexible Systems
Comments: 11 pages, 14 figures
Subjects: Optimization and Control (math.OC); Applications (stat.AP)

This article proposes a universal simulation platform for simulating systems undergoing duress. In other words, this paper introduces a total simulation package which includes a number of methods of simulating the flexibility of a given system. This platform includes detailed procedures for simulating a flexible link by a numerical method called finite difference method. In order to verify the effectiveness of the proposed process, two examples are covered in different situations to discuss the importance of boundary control and mesh selection in the way of ensuring the stability of the system. In addition, a graphical user interface (GUI) application called the SimuFlex is designed having a selection of methods that the user can choose along with the parameters of the controllers that can be easily manipulated from the GUI.

### Replacements for Fri, 20 Oct 17

[24]  arXiv:1410.3351 (replaced) [pdf, ps, other]
Title: Ricci Curvature and the Manifold Learning Problem
Subjects: Differential Geometry (math.DG); Learning (cs.LG); Metric Geometry (math.MG); Machine Learning (stat.ML)
[25]  arXiv:1503.07102 (replaced) [pdf, ps, other]
Title: A Variant of AIC based on the Bayesian Marginal Likelihood
Subjects: Methodology (stat.ME)
[26]  arXiv:1604.03325 (replaced) [pdf]
Title: Using Extreme Value Theory for Determining the Probability of Carrington-Like Solar Flares
Comments: 13 pages, 4 figures; updated content following reviewer feedback
Subjects: Applications (stat.AP); Solar and Stellar Astrophysics (astro-ph.SR); Data Analysis, Statistics and Probability (physics.data-an)
[27]  arXiv:1609.06805 (replaced) [pdf, other]
Title: Most recent changepoint detection in Panel data
Subjects: Applications (stat.AP)
[28]  arXiv:1704.01896 (replaced) [pdf, other]
Title: Compositional Nonparametric Prediction: Statistical Efficiency and Greedy Regression Algorithm
Subjects: Machine Learning (stat.ML); Learning (cs.LG)
[29]  arXiv:1705.00554 (replaced) [pdf, other]
Title: A structural Markov property for decomposable graph laws that allows control of clique intersections
Comments: 10 pages, 3 figures; updated from V1 following journal review, new more explicit title and added section on inference
Subjects: Computation (stat.CO)
[30]  arXiv:1706.06344 (replaced) [pdf, ps, other]
Title: Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods
Comments: Supplementary material attached. To view attachments, please download and extract the gzzipped source file listed under "Other formats"
Subjects: Computation (stat.CO)
[31]  arXiv:1708.09427 (replaced) [pdf]
Title: End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
Authors: Li Shen
Comments: urgent fix for challenge policy compliance; fix some typos; add acknowledgements
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
[32]  arXiv:1709.09599 (replaced) [pdf]
Title: The Nu Class of Low-Degree-Truncated Rational Multifunctions. Ic. IMSPE-optimal designs with circular-disk prediction domains
Comments: 16 pages, 8 figures
Subjects: Methodology (stat.ME)
[33]  arXiv:1710.01410 (replaced) [pdf, other]
Title: Learning Registered Point Processes from Idiosyncratic Observations
Subjects: Machine Learning (stat.ML)
[34]  arXiv:1710.02761 (replaced) [pdf, other]
Title: Fréchet Analysis Of Variance For Random Objects
Subjects: Statistics Theory (math.ST); Metric Geometry (math.MG)
[35]  arXiv:1710.06660 (replaced) [pdf, other]
Title: Variable selection for the prediction of C[0,1]-valued AR processes using RKHS
Comments: 29 pages, 8 figures
Subjects: Methodology (stat.ME)
[ total of 35 entries: 1-35 ]
[ showing up to 2000 entries per page: fewer | more ]

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