Search results for "Identifiability"
showing 10 items of 35 documents
On the sign recovery by LASSO, thresholded LASSO and thresholded Basis Pursuit Denoising
2020
Basis Pursuit (BP), Basis Pursuit DeNoising (BPDN), and LASSO are popular methods for identifyingimportant predictors in the high-dimensional linear regression model Y = Xβ + ε. By definition, whenε = 0, BP uniquely recovers β when Xβ = Xb and β different than b implies L1 norm of β is smaller than the L1 norm of b (identifiability condition). Furthermore, LASSO can recover the sign of β only under a much stronger irrepresentability condition. Meanwhile, it is known that the model selection properties of LASSO can be improved by hard-thresholdingits estimates. This article supports these findings by proving that thresholded LASSO, thresholded BPDNand thresholded BP recover the sign of β in …
Closedness Properties in EX-Identification of Recursive Functions
1998
In this paper we investigate in which cases unions of identifiable classes of recursive functions are also necessarily identifiable. We consider identification in the limit with bounds on mindchanges and anomalies. Though not closed under the set union, these identification types still have features resembling closedness. For each of them we find such n that 1) if every union of n - 1 classes out of U1;, . . ., Un is identifiable, so is the union of all n classes; 2) there are such classes U1;, . . ., Un-1 that every union of n-2 classes out of them is identifiable, while the union of n - 1 classes is not. We show that by finding these n we can distinguish which requirements put on the iden…
Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster
2013
16 páginas, 6 figuras, 1 tabla
Identifying Causal Effects with the R Package causaleffect
2017
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately eit…
Localized potentials in electrical impedance tomography
2008
In this work we study localized electric potentials that have an arbitrarily high energy on some given subset of a domain and low energy on another. We show that such potentials exist for general L ∞ -conductivities in almost arbitrarily shaped subregions of a domain, as long as these regions are connected to the boundary and a unique continuation principle is satisfied. From this we deduce a simple, but new, theoretical identifiability result for the famous Calderon problem with partial data. We also show how to con- struct such potentials numerically and use a connection with the factorization method to derive a new non-iterative algorithm for the detection of inclusions in electrical imp…
Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data
2019
Summary In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixtur…
Structure Learning in Nested Effects Models
2007
Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we…
Numeral classifiers in East Asia
2017
AbstractThe understanding of the functional range of numeral classifiers requires a combined effort from different theoretical approaches and the integration of findings from syntax, semantics and pragmatics. This article demonstrates how such an integrative perspective creates new insights into classifiers in Sinitic languages in the fields of counting and word order, the expression of definiteness and indefiniteness and the syntax of verbal classification.
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
2021
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…
Identifiability analysis for pressure sensors positioning
2017
The identifiability analysis is investigated as sampling design method aimed to the leakage detection in looped water distribution networks. The preliminary ranking of the candidate nodes for the pressure sensors positioning is performed by running several hydraulic simulations and calculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used to perform the identifiability analysis by calculating the collinearity index which provides the maximum number of sensors and their location into the network. The index selects the nodes according to their sensitivities to several leakages scenarios, simulated in EPANET by changing the emitter coefficient of the …