How to reduce the number of rating scale items without predictability loss? [PDF]
Scientometrics, 2017 Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The "area under the receiver operator
Koczkodaj WW +7 more
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2000 Mathematics Subject Classification: Primary 62C99, sec-ondary 62C10, 62C20, 62J05
, 2016 The paper deals with recovering an unknown vector β ∈ R^p based on the observations Y = Xβ + ∈ξ and Z = X + σζ, where X is an unknown n×p-matrix with n ≥ p, ξ ∈ R^p is a standard white Gaussian noise, ζ is a n × p-matrix with i.i.d. standard Gaussian entries, and ∈, σ ∈ R^+ are known noise levels. It is assumed that X has a large condition number and p
Yu. Golubev, Th. Zimolo
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Personalized decision making – A conceptual introduction
Journal of Causal Inference, 2023 Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a subpopulation resembling that individual.
Mueller Scott, Pearl Judea
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Decision-theoretic foundations for statistical causality
Journal of Causal Inference, 2021 We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions.
Dawid Philip
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Decision-theoretic foundations for statistical causality: Response to Pearl
Journal of Causal Inference, 2022 I thank Judea Pearl for his discussion of my paper and respond to the points he raises. In particular, his attachment to unaugmented directed acyclic graphs has led to a misapprehension of my own proposals. I also discuss the possibilities for developing
Dawid Philip
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Decision-theoretic foundations for statistical causality: Response to Shpitser
Journal of Causal Inference, 2022 I thank Ilya Shpitser for his comments on my article, and discuss the use of models with restricted interventions.
Dawid Philip
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Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”
Journal of Causal Inference, 2022 In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic ...
Pearl Judea
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A unified approach for covariance matrix estimation under Stein loss [PDF]
, 2021 In this paper, we address the problem of estimating a covariance matrix of a multivariate Gaussian distribution, relative to a Stein loss function, from a decision theoretic point of view. We investigate the case where the covariance matrix is invertible
Haddouche, Anis M., Lu, Wei
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A General-Theory Of Asymptotic Consistency For Subset-Selection With Applications [PDF]
, 1984 Mathematic
Bjornstad, Jan F.
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Why scoring functions cannot assess tail properties [PDF]
, 2019 Motivated by the growing interest in sound forecast evaluation techniques with an emphasis on distribution tails rather than average behaviour, we investigate a fundamental question arising in this context: Can statistical features of distribution tails ...
Brehmer, Jonas, Strokorb, Kirstin
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