Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Slawski, Martin P."'
In small area estimation different data sources are integrated in order to produce reliable estimates of target parameters (e.g., a mean or a proportion) for a collection of small subsets (areas) of a finite population. Regression models such as the
Externí odkaz:
http://arxiv.org/abs/2405.20149
Autor:
Slawski, Martin, West, Brady T., Bukke, Priyanjali, Diao, Guoqing, Wang, Zhenbang, Ben-David, Emanuel
Data sets obtained from linking multiple files are frequently affected by mismatch error, as a result of non-unique or noisy identifiers used during record linkage. Accounting for such mismatch error in downstream analysis performed on the linked fil
Externí odkaz:
http://arxiv.org/abs/2306.00909
We propose a new method that uses tensor completion to estimate causal effects with multivariate longitudinal data, data in which multiple outcomes are observed for each unit and time period. Our motivation is to estimate the number of COVID-19 fatal
Externí odkaz:
http://arxiv.org/abs/2203.04689
Autor:
Slawski, Martin, Sen, Bodhisattva
Suppose that we have a regression problem with response variable Y in $\mathbb{R}^d$ and predictor X in $\mathbb{R}^d$, for $d \geq 1$. In permuted or unlinked regression we have access to separate unordered data on X and Y, as opposed to data on (X,
Externí odkaz:
http://arxiv.org/abs/2201.03528
In the analysis of data sets consisting of (X, Y)-pairs, a tacit assumption is that each pair corresponds to the same observation unit. If, however, such pairs are obtained via record linkage of two files, this assumption can be violated as a result
Externí odkaz:
http://arxiv.org/abs/2111.01767
Identification of matching records in multiple files can be a challenging and error-prone task. Linkage error can considerably affect subsequent statistical analysis based on the resulting linked file. Several recent papers have studied post-linkage
Externí odkaz:
http://arxiv.org/abs/2010.00181
Federated learning (FL) is a machine learning paradigm where a shared central model is learned across distributed edge devices while the training data remains on these devices. Federated Averaging (FedAvg) is the leading optimization method for train
Externí odkaz:
http://arxiv.org/abs/1911.02134
Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertai
Externí odkaz:
http://arxiv.org/abs/1910.01623
In "Unlabeled Sensing", one observes a set of linear measurements of an underlying signal with incomplete or missing information about their ordering, which can be modeled in terms of an unknown permutation. Previous work on the case of a single nois
Externí odkaz:
http://arxiv.org/abs/1909.02496
A tacit assumption in linear regression is that (response, predictor)-pairs correspond to identical observational units. A series of recent works have studied scenarios in which this assumption is violated under terms such as ``Unlabeled Sensing and
Externí odkaz:
http://arxiv.org/abs/1907.07148