Hasil untuk "stat.OT"

A. Philip Dawid

I thank Thomas Richardson and James Robins for their discussion of my paper, and discuss the similarities and differences between their approach to causal modelling, based on single world intervention graphs, and my own decision-theoretic approach.

Arturo Erdely, Eduardo Gutiérrez-Peña

Course notes about an introduction to Bayesian Statistics. First, an explanation of the bayesian paradigm is motivated and explained in detail (first three chapters). Then, a brief introduction to the basics about Decision Theory in chapter four, which is self contained, with the purpose of introducing parametrica bayesian inference as a decision problem in chapter five.

Helmut Kahl

Determinant formulas are presented for: a certain positive semidefinite, hermitian matrix; the loss value of multilinear regression; the multiple linear regression coefficient.

Philip B. Stark

While P-values are widely abused, they are a useful tool for many purposes; banning them is analogous to banning scalpels because most people do not know how to perform surgery. Many reported P-values are not genuine P-values, for a variety of reasons. Perhaps the most widespread and pernicious problem is the Type III error of testing a statistical hypothesis that has little or no connection to the scientific hypothesis.

Russell J. Bowater

Integrated organic inference (IOI) is discussed in a concise and informal way with the aim that the reader is given the gist of what this approach to statistical inference is about as well as given pointers to further reading.

Yudi Pawitan, Arvid Sjölander

Multiple testing problems arise naturally in scientific studies because of the need to capture or convey more information with more variables. The literature is enormous, but the emphasis is primarily methodological, providing numerous methods with their mathematical justification and practical implementation. Our aim is to highlight the logical issues involved in the application of multiple testing adjustment.

Romain Legrand

Educational guide focused on the statistical treatment of measurement uncertainties. The conditions of application of current practices are detailed and precised: mean values, central limit theorem, linear regression. The last two chapters are devoted to an introduction to the Bayesian inference and a series of application cases: machine failure date, elimination of a background noise, linear adjustment with elimination of outliers.

Ismihan Bayramoglu

The focus of this paper is the random sequences in the form $\{X_{0},X_{1},$ $X_{n}=X_{n-2}+X_{n-1},n=2,3,..\dot{\}},$ referred to as Fibonacci Random Sequence (FRS). The initial random variables $X_{0}$ and $X_{1}$ are assumed to be absolutely continuous with joint probability density function (pdf) $f_{X_{0},X_{1}}.$ The FRS is completely determined by $X_{0}$ and $X_{1}$ and the members of Fibonacci sequence $\digamma \equiv\{0,1,1,2,3,5,8,13,21,34,55,89,144,...\}.$ We examine the distributional and limit properties of the random sequence $X_{n},n=0,1,2,...$ .

G. Taraldsen, B. H. Lindqvist

The following discussion is inspired by the paper Nonparametric generalized fiducial inference for survival functions under censoring by Cui and Hannig. The discussion consists of comments on the results, but also indicates it's importance more generally in the context of fiducial inference. A two page introduction to fiducial inference is given to provide a context.

Giovanni Parmigiani

The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the situation where some data points, falling in an indeterminacy region, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds under indeterminacy; and visual summarization of the large number of possibilities arising from different choices of indeterminacy zones.

Peter Selinger, Kristopher Tapp

Chen and Risen pointed out a logical flaw affecting the conclusions of a number of past experiments that used the free-choice paradigm to measure choice-induced attitude change. They went on to design and implement a free-choice experiment that used a novel type of control group in order to avoid this logical pitfall. In this paper, we describe a method by which a free-choice experiment can be correctly conducted even without a control group.

Anthony D. Blaom

The classical Cramér-Rao inequality gives a lower bound for the variance of a unbiased estimator of an unknown parameter, in some statistical model of a random process. In this note we rewrite the statment and proof of the bound using contemporary geometric language.

Ricardo Almeida

The aim of this work is to show, based on concrete data observation, that the choice of the fractional derivative when modelling a problem is relevant for the accuracy of a method. Using the least squares fitting technique, we determine the order of the fractional differential equation that better describes the experimental data, for different types of fractional derivatives.

Laurie Davies

Models are consistently treated as approximations and all procedures are consistent with this. They do not treat the model as being true. In this context $p$-values are one measure of approximation, a small $p$-value indicating a poor approximation. Approximation regions are defined and distinguished from confidence regions.

Dominique Pepin

The objective of this paper is to examine the assertion that the word "statistics" would have been used for the first time in the 17th century, in a report written by Claude Bouchu, administrator of Bourgogne. A historical and bibliographical analysis is carried out to judge the credibility of this thesis. The physical inspection of the report then makes it possible to bring a final answer.

Carole Bernard, Steven Vanduffel

In this note, we give an explicit expression for the quantile of a mixture of two random variables. We carefully examine all possible cases of discrete and continuous variables with possibly unbounded support. The result is useful for finding bounds on the Value-at-Risk of risky portfolios when only partial information is available (Bernard and Vanduffel (2014)).

Miron L. Straf, Judith M. Tanur

The following conversation is based in part on a transcript of a 2009 interview funded by Pfizer Global Research-Connecticut, the American Statistical Association and the Department of Statistics at the University of Connecticut-Storrs as part of the "Conversations with Distinguished Statisticians in Memory of Professor Harry O. Posten".

Yixin Fang, Yuanjia Wang, Xin Huang

We derive a divergence formula for a group of regularization methods with an L2 constraint. The formula is useful for regularization parameter selection, because it provides an unbiased estimate for the number of degrees of freedom. We begin with deriving the formula for smoothing splines and then extend it to other settings such as penalized splines, ridge regression, and functional linear regression.

Eric Bliss

In this paper, I will demonstrate a new perspective on the Two Envelope Problem. I hope to show with convincing clarity how the paradox results from an inherent problem pertaining to the interpretation of Bayesian probability. Specifically, a subjective probability that is inconsistent with reality can mislead reasoning based on Bayesian decision theory.

H. M. Bharath

In this paper, we present a possible theoretical explanation for benford's law. We develop a recursive relation between the probabilities, using simple intuitive ideas. We first use numerical solutions of this recursion and verify that the solutions converge to the benford's law. Finally we solve the recursion analytically to yeild the benford's law for base 2.