pinecone/core-2020-05-10-deduplication huggingface dataset:
Optimum Design of Ceramic Spray Coating Evaluated in Terms of Intensity of Singular Stress Field

Hearth rolls are the most important tools in continuous annealing line to produce thin steel sheet. Usually, the roll surface is coated by ceramics using thermal spraying. However, material mismatch and temperature change may cause thermal stress leading to the failure of the coating and deteriorating high adhesive strength and wears resistance. Therefore, it is very important to improve the debonding strength by optimizing the design of spray coating. The traditional method of evaluating the debonding strength of spray coating is prescribed as JIS H8304 thermal test. This paper focuses on the intensity of singular stress field (ISSF) at the end of interface since few research is available in terms of ISSF. Previously, the authors considered the JIS specimen assuming a plane strain. However, since the hearth roll is cylindrical and axi-symmetric, the difference between plane strain and axi-symmetric models should be clarified. Thus, in this paper, the axi-symmetric model is newly considered and the optimum design is discussed by varying the coating material and the coating thickness. Also the present results are compared with the previous results of JIS specimen so that the difference between actual model and JIS specimen can be clarified to contribute the design and evaluation of the real rolls
Confidence bands for smoothness in nonparametric regression

The choice of the smoothing parameter in nonparametric regression is critical to the form of the estimated curve and any inference that follows. Many methods are available that will generate a single choice for this parameter. Here we argue that the considerable uncertainty in this choice should be explicitly represented. The construction of standard simultaneous confidence bands in nonparametric regression often requires difficult mathematical arguments. We question their practical utility, presenting several deficiencies. We propose a new kind of confidence band that reflects the uncertainty regarding the smoothness of the estimate
Exploiting the Quantile Optimality Ratio to Obtain Better Confidence
  Intervals for Quantiles

A standard approach to confidence intervals for quantiles requires good
estimates of the quantile density. The optimal bandwidth for kernel estimation
of the quantile density depends on an underlying location-scale family only
through the quantile optimality ratio (QOR), which is the starting point for
our results. While the QOR is not distribution-free, it turns out that what is
optimal for one family often works quite well for families having similar
shape. This allows one to rely on a single representative QOR if one has a
rough idea of the distributional shape. Another option that we explore assumes
the data can be modeled by the highly flexible generalized lambda distribution
(GLD), already studied by others, and we show that using the QOR for the
estimated GLD can lead to more than competitive intervals. Confidence intervals
for the difference between quantiles from independent populations are also
considered, with an application to heart rate data.Comment: 27 pages, 8 figure
On the smallest eigenvalues of covariance matrices of multivariate spatial processes

There has been a growing interest in providing models for multivariate spatial processes. A majority of these models specify a parametric matrix covariance function. Based on observations, the parameters are estimated by maximum likelihood or variants thereof. While the asymptotic properties of maximum likelihood estimators for univariate spatial processes have been analyzed in detail, maximum likelihood estimators for multivariate spatial processes have not received their deserved attention yet. In this article, we consider the classical increasing-domain asymptotic setting restricting the minimum distance between the locations. Then, one of the main components to be studied from a theoretical point of view is the asymptotic positive definiteness of the underlying covariance matrix. Based on very weak assumptions on the matrix covariance function, we show that the smallest eigenvalue of the covariance matrix is asymptotically bounded away from zero. Several practical implications are discussed as well
Hidden Gibbs random fields model selection using Block Likelihood
  Information Criterion

Performing model selection between Gibbs random fields is a very challenging
task. Indeed, due to the Markovian dependence structure, the normalizing
constant of the fields cannot be computed using standard analytical or
numerical methods. Furthermore, such unobserved fields cannot be integrated out
and the likelihood evaluztion is a doubly intractable problem. This forms a
central issue to pick the model that best fits an observed data. We introduce a
new approximate version of the Bayesian Information Criterion. We partition the
lattice into continuous rectangular blocks and we approximate the probability
measure of the hidden Gibbs field by the product of some Gibbs distributions
over the blocks. On that basis, we estimate the likelihood and derive the Block
Likelihood Information Criterion (BLIC) that answers model choice questions
such as the selection of the dependency structure or the number of latent
states. We study the performances of BLIC for those questions. In addition, we
present a comparison with ABC algorithms to point out that the novel criterion
offers a better trade-off between time efficiency and reliable results
Pairwise Markov properties for regression graphs

With a sequence of regressions, one may generate joint probability
distributions. One starts with a joint, marginal distribution of context
variables having possibly a concentration graph structure and continues with an
ordered sequence of conditional distributions, named regressions in joint
responses. The involved random variables may be discrete, continuous or of both
types. Such a generating process specifies for each response a conditioning set
which contains just its regressor variables and it leads to at least one valid
ordering of all nodes in the corresponding regression graph which has three
types of edge; one for undirected dependences among context variables, another
for undirected dependences among joint responses and one for any directed
dependence of a response on a regressor variable. For this regression graph,
there are several definitions of pairwise Markov properties, where each
interprets the conditional independence associated with a missing edge in the
graph in a different way. We explain how these properties arise, prove their
equivalence for compositional graphoids and point at the equivalence of each
one of them to the global Markov property.Comment: 11 pages, 1 figur
Asymptotic properties of adaptive group Lasso for sparse reduced rank
  regression

This paper studies the asymptotic properties of the penalized least squares
estimator using an adaptive group Lasso penalty for the reduced rank
regression. The group Lasso penalty is defined in the way that the regression
coefficients corresponding to each predictor are treated as one group. It is
shown that under certain regularity conditions, the estimator can achieve the
minimax optimal rate of convergence. Moreover, the variable selection
consistency can also be achieved, that is, the relevant predictors can be
identified with probability approaching one. In the asymptotic theory, the
number of response variables, the number of predictors, and the rank number are
allowed to grow to infinity with the sample size
A test for second-order stationarity of time series based on
  unsystematic sub-samples

In this paper, we introduce a new method for testing the stationarity of time
series, where the test statistic is obtained from measuring and maximising the
difference in the second-order structure over pairs of randomly drawn
intervals. The asymptotic normality of the test statistic is established for
both Gaussian and a range of non-Gaussian time series, and a bootstrap
procedure is proposed for estimating the variance of the main statistics.
Further, we show the consistency of our test under local alternatives. Due to
the flexibility inherent in the random, unsystematic sub-samples used for test
statistic construction, the proposed method is able to identify the intervals
of significant departure from the stationarity without any dyadic constraints,
which is an advantage over other tests employing systematic designs. We
demonstrate its good finite sample performance on both simulated and real data,
particularly in detecting localised departure from the stationarity
Finite Mixtures of Skewed Matrix Variate Distributions

Clustering is the process of finding underlying group structures in data.
Although mixture model-based clustering is firmly established in the
multivariate case, there is a relative paucity of work on matrix variate
distributions and none for clustering with mixtures of skewed matrix variate
distributions. Four finite mixtures of skewed matrix variate distributions are
considered. Parameter estimation is carried out using an
expectation-conditional maximization algorithm, and both simulated and real
data are used for illustration
A determinant-free method to simulate the parameters of large Gaussian
  fields

We propose a determinant-free approach for simulation-based Bayesian
inference in high-dimensional Gaussian models. We introduce auxiliary variables
with covariance equal to the inverse covariance of the model. The joint
probability of the auxiliary model can be computed without evaluating
determinants, which are often hard to compute in high dimensions. We develop a
Markov chain Monte Carlo sampling scheme for the auxiliary model that requires
no more than the application of inverse-matrix-square-roots and the solution of
linear systems. These operations can be performed at large scales with rational
approximations. We provide an empirical study on both synthetic and real-world
data for sparse Gaussian processes and for large-scale Gaussian Markov random
fields.Comment: 16 pages, 3 figure
A Comparison of Resampling and Recursive Partitioning Methods in Random
  Forest for Estimating the Asymptotic Variance Using the Infinitesimal
  Jackknife

The infinitesimal jackknife (IJ) has recently been applied to the random
forest to estimate its prediction variance. These theorems were verified under
a traditional random forest framework which uses classification and regression
trees (CART) and bootstrap resampling. However, random forests using
conditional inference (CI) trees and subsampling have been found to be not
prone to variable selection bias. Here, we conduct simulation experiments using
a novel approach to explore the applicability of the IJ to random forests using
variations on the resampling method and base learner. Test data points were
simulated and each trained using random forest on one hundred simulated
training data sets using different combinations of resampling and base
learners. Using CI trees instead of traditional CART trees as well as using
subsampling instead of bootstrap sampling resulted in a much more accurate
estimation of prediction variance when using the IJ. The random forest
variations here have been incorporated into an open source software package for
the R programming language
Fast and accurate Bayesian model criticism and conflict diagnostics
  using R-INLA

Bayesian hierarchical models are increasingly popular for realistic modelling
and analysis of complex data. This trend is accompanied by the need for
flexible, general, and computationally efficient methods for model criticism
and conflict detection. Usually, a Bayesian hierarchical model incorporates a
grouping of the individual data points, for example individuals in repeated
measurement data. In such cases, the following question arises: Are any of the
groups "outliers", or in conflict with the remaining groups? Existing general
approaches aiming to answer such questions tend to be extremely computationally
demanding when model fitting is based on MCMC. We show how group-level model
criticism and conflict detection can be done quickly and accurately through
integrated nested Laplace approximations (INLA). The new method is implemented
as a part of the open source R-INLA package for Bayesian computing
(http://r-inla.org)
Merging $K$-means with hierarchical clustering for identifying
  general-shaped groups

Clustering partitions a dataset such that observations placed together in a
group are similar but different from those in other groups. Hierarchical and
$K$-means clustering are two approaches but have different strengths and
weaknesses. For instance, hierarchical clustering identifies groups in a
tree-like structure but suffers from computational complexity in large datasets
while $K$-means clustering is efficient but designed to identify homogeneous
spherically-shaped clusters. We present a hybrid non-parametric clustering
approach that amalgamates the two methods to identify general-shaped clusters
and that can be applied to larger datasets. Specifically, we first partition
the dataset into spherical groups using $K$-means. We next merge these groups
using hierarchical methods with a data-driven distance measure as a stopping
criterion. Our proposal has the potential to reveal groups with general shapes
and structure in a dataset. We demonstrate good performance on several
simulated and real datasets.Comment: 16 pages, 1 table, 9 figures; accepted for publication in Sta
Nonconvex penalized multitask regression using data depth-based
  penalties

We propose a new class of nonconvex penalty functions, based on data depth
functions, for multitask sparse penalized regression. These penalties quantify
the relative position of rows of the coefficient matrix from a fixed
distribution centered at the origin. We derive the theoretical properties of an
approximate one-step sparse estimator of the coefficient matrix using local
linear approximation of the penalty function, and provide algorithm for its
computation. For orthogonal design and independent responses, the resulting
thresholding rule enjoys near-minimax optimal risk performance, similar to the
adaptive lasso (Zou, 2006). A simulation study and real data analysis
demonstrate its effectiveness compared to some of the present methods that
provide sparse solutions in multivariate regression
Flexible Clustering for High-Dimensional Data via Mixtures of Joint
  Generalized Hyperbolic Models

A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced
for asymmetric clustering for high-dimensional data. The MJGHD approach takes
into account the cluster-specific subspace, thereby limiting the number of
parameters to estimate while also facilitating visualization of results.
Identifiability is discussed, and a multi-cycle ECM algorithm is outlined for
parameter estimation. The MJGHD approach is illustrated on two real data sets,
where the Bayesian information criterion is used for model selection
Preserving Differential Privacy Between Features in Distributed
  Estimation

Privacy is crucial in many applications of machine learning. Legal, ethical
and societal issues restrict the sharing of sensitive data making it difficult
to learn from datasets that are partitioned between many parties. One important
instance of such a distributed setting arises when information about each
record in the dataset is held by different data owners (the design matrix is
"vertically-partitioned").
  In this setting few approaches exist for private data sharing for the
purposes of statistical estimation and the classical setup of differential
privacy with a "trusted curator" preparing the data does not apply. We work
with the notion of $(\epsilon,\delta)$-distributed differential privacy which
extends single-party differential privacy to the distributed,
vertically-partitioned case. We propose PriDE, a scalable framework for
distributed estimation where each party communicates perturbed random
projections of their locally held features ensuring
$(\epsilon,\delta)$-distributed differential privacy is preserved. For
$\ell_2$-penalized supervised learning problems PriDE has bounded estimation
error compared with the optimal estimates obtained without privacy constraints
in the non-distributed setting. We confirm this empirically on real world and
synthetic datasets
A Statistical View of Learning in the Centipede Game

In this article we evaluate the statistical evidence that a population of
students learn about the sub-game perfect Nash equilibrium of the centipede
game via repeated play of the game. This is done by formulating a model in
which a player's error in assessing the utility of decisions changes as they
gain experience with the game. We first estimate parameters in a statistical
model where the probabilities of choices of the players are given by a Quantal
Response Equilibrium (QRE) (McKelvey and Palfrey, 1995, 1996, 1998), but are
allowed to change with repeated play. This model gives a better fit to the data
than similar models previously considered. However, substantial correlation of
outcomes of games having a common player suggests that a statistical model that
captures within-subject correlation is more appropriate. Thus we then estimate
parameters in a model which allows for within-player correlation of decisions
and rates of learning. Through out the paper we also consider and compare the
use of randomization tests and posterior predictive tests in the context of
exploratory and confirmatory data analyses
Mixtures of Common Skew-t Factor Analyzers

A mixture of common skew-t factor analyzers model is introduced for
model-based clustering of high-dimensional data. By assuming common component
factor loadings, this model allows clustering to be performed in the presence
of a large number of mixture components or when the number of dimensions is too
large to be well-modelled by the mixtures of factor analyzers model or a
variant thereof. Furthermore, assuming that the component densities follow a
skew-t distribution allows robust clustering of skewed data. The alternating
expectation-conditional maximization algorithm is employed for parameter
estimation. We demonstrate excellent clustering performance when our model is
applied to real and simulated data.This paper marks the first time that skewed
common factors have been used
Propensity score estimation in the presence of length‐biased sampling: a non‐parametric adjustment approach

The pervasive use of prevalent cohort studies on disease duration increasingly calls for an appropriate methodology to account for the biases that invariably accompany samples formed by such data. It is well known, for example, that subjects with shorter lifetime are less likely to be present in such studies. Moreover, certain covariate values could be preferentially selected into the sample, being linked to the long‐term survivors. The existing methodology for estimating the propensity score using data collected on prevalent cases requires the correct conditional survival/hazard function given the treatment and covariates. This requirement can be alleviated if the disease under study has stationary incidence, the so‐called stationarity assumption. We propose a non‐parametric adjustment technique based on a weighted estimating equation for estimating the propensity score, which does not require modeling the conditional survival/hazard function when the stationarity assumption holds. The estimator's large‐sample properties are established, and its small‐sample behavior is studied via simulation. The estimated propensity score is utilized to estimate the survival curves. Copyright © 2014 John Wiley & Sons, Ltd
Spline approximations to conditional Archimedean copula

We propose a flexible copula model to describe changes with a covariate in
the dependence structure of (conditionally exchangeable) random variables. The
starting point is a spline approximation to the generator of an Archimedean
copula. Changes in the dependence structure with a covariate $x$ are modelled
by flexible regression of the spline coefficients on $x$. The performances and
properties of the spline estimate of the reference generator and the abilities
of these conditional models to approximate conditional copulas are studied
through simulations. Inference is made using Bayesian arguments with posterior
distributions explored using importance sampling or adaptive MCMC algorithms.
The modelling strategy is illustrated with two examples.Comment: Key words: Conditional copula ; Archimedean copula ; B-spline
Enhanced Security Checks at Airports: Minimizing Time to Detection or Probability of Escape?

A recent featured article in Significance on enhanced security checks at airports presented an argument that permitted a sampling probability to exceed one.  The argument itself therefore cannot be valid, regardless of whether its intended conclusion is justifiable on other grounds.  The fact that such an argument could pass the security check of a statistical publication reminds us of the grand challenge we still face: making principled statistical reasoning a routine vocabulary of our civilization.  An attempt to correct the argument also demonstrates how oversights can inspire insights: we show that in a design-based context, the impact of prior can persist even if we have the resources to collect an infinite amount of data.Statistic
Correcting for non-ignorable missingness in smoking trends

Data missing not at random (MNAR) is a major challenge in survey sampling. We
propose an approach based on registry data to deal with non-ignorable
missingness in health examination surveys. The approach relies on follow-up
data available from administrative registers several years after the survey.
For illustration we use data on smoking prevalence in Finnish National FINRISK
study conducted in 1972-1997. The data consist of measured survey information
including missingness indicators, register-based background information and
register-based time-to-disease survival data. The parameters of missingness
mechanism are estimable with these data although the original survey data are
MNAR. The underlying data generation process is modelled by a Bayesian model.
The results indicate that the estimated smoking prevalence rates in Finland may
be significantly affected by missing data.Comment: in Stat, 201
Experimental and modeling studies on the synthesis and properties of higher fatty esters of corn starch

This paper describes a systematic study on the synthesis of higher fatty esters of corn starch (starch laurate and starch stÃ©arate) by using the corresponding vinyl esters. The reactions were carried out in DMSO using basic catalysts (Na2HPO4, K2CO3, and Naacetate). The effect of the process variables (vinyl ester to starch ratio, catalyst intake, reaction temperature and type of the catalyst) on the degree of substitution (DS) of the starch laurate and starch stearate esters was determined by performing a total of 54 experiments. The results were adequately modeled using a non-linear multivariable regression model (R2â‰¥0. 96). The basicity of the catalyst and the reaction temperature have the highest impact on the product DS. The thermal and mechanical properties of some representative product samples were determined. High-DS products (DS = 2.26-2.39) are totally amorphous whereas the low-DS ones (DS = 1.45-1.75) are still partially crystalline. The thermal stability of the esterlfied products is higher than that of native starch. Mechanical tests show that the products have a tensile strength (stress at break) between 2.7-3.5 MPa, elongation at break of 3-26%, and modulus of elasticity of 46-113 MPa.