1,822 research outputs found
Identification Robust Confidence Sets Methods for Inference on Parameter Ratios and their Application to Estimating Value-of-Time
The problem of constructing confidence set estimates for parameter ratios arises in a variety of econometrics contexts; these include value-of-time estimation in transportation research and inference on elasticities given several model specifications. Even when the model under consideration is identifiable, parameter ratios involve a possibly discontinuous parameter transformation that becomes ill-behaved as the denominator parameter approaches zero. More precisely, the parameter ratio is not identified over the whole parameter space: it is locally almost unidentified or (equivalently) weakly identified over a subset of the parameter space. It is well known that such situations can strongly affect the distributions of estimators and test statistics, leading to the failure of standard asymptotic approximations, as shown by Dufour. Here, we provide explicit solutions for projection-based simultaneous confidence sets for ratios of parameters when the joint confidence set is obtained through a generalized Fieller approach. A simulation study for a ratio of slope parameters in a simple binary probit model shows that the coverage rate of the Fieller's confidence interval is immune to weak identification whereas the confidence interval based on the delta-method performs poorly, even when the sample size is large. The procedures are examined in illustrative empirical models, with a focus on choice modelsconfidence interval; generalized Fieller's theorem; delta-method; weak identification; ratio of parameters.
Resolution of Peller's problem concerning Koplienko-Neidhardt trace formulae
A formula for the norm of a bilinear Schur multiplier acting from the
Cartesian product of two copies of the
Hilbert-Schmidt classes into the trace class is established in
terms of linear Schur multipliers acting on the space of
all compact operators. Using this formula, we resolve Peller's problem on
Koplienko-Neidhardt trace formulae. Namely, we prove that there exist a twice
continuously differentiable function with a bounded second derivative, a
self-adjoint (unbounded) operator and a self-adjoint operator such that
f(A+B)-f(A)-\frac{d}{dt}(f(A+tB))\big\vert_{t=0}\notin \mathcal S^1. $
Peller's problem concerning Koplienko-Neidhardt trace formulae: the unitary case
We prove the existence of a complex valued -function on the unit circle,
a unitary operator U and a self-adjoint operator Z in the Hilbert-Schmidt class
, such that the perturbated operator does not belong to the
space of trace class operators. This resolves a problem of Peller
concerning the validity of the Koplienko-Neidhardt trace formula for unitaries
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
Unconstrained handwritten text recognition is a challenging computer vision
task. It is traditionally handled by a two-step approach, combining line
segmentation followed by text line recognition. For the first time, we propose
an end-to-end segmentation-free architecture for the task of handwritten
document recognition: the Document Attention Network. In addition to text
recognition, the model is trained to label text parts using begin and end tags
in an XML-like fashion. This model is made up of an FCN encoder for feature
extraction and a stack of transformer decoder layers for a recurrent
token-by-token prediction process. It takes whole text documents as input and
sequentially outputs characters, as well as logical layout tokens. Contrary to
the existing segmentation-based approaches, the model is trained without using
any segmentation label. We achieve competitive results on the READ 2016 dataset
at page level, as well as double-page level with a CER of 3.43% and 3.70%,
respectively. We also provide results for the RIMES 2009 dataset at page level,
reaching 4.54% of CER.
We provide all source code and pre-trained model weights at
https://github.com/FactoDeepLearning/DAN
Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations
A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards
Compensation magnétique de pesanteur dans des fluides : synthÚse des performances et contraintes
La communication présente les principauxrésultats théoriques et expérimentaux obtenus depuis unequinzaine d'années dans le domaine de la compensationmagnétique de pesanteur. Cette technique, destinéeprincipalement à des études de comportement de fluides enenvironnement spatial, utilise des sources de champ magnétiquegénéralement élevé. Les avantages et les limites de la lévitationsont présentés ; son application à des problÚmes decomportement de fluides sous forme diphasique est décrite.</p
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