305,787 research outputs found
On iterative adjustment of responses for the reduction of bias in binary regression models
No full textThe adjustment of the binomial data by small constants is a common practice in statistical
modelling, for avoiding sparseness issues and, historically, for improving the asymptotic properties
of the estimators. However, there are two main disadvantages with such practice: i) there
is not a universal constant adjustment that results estimators with optimal asymptotic properties
for all possible modelling settings, and ii) the resultant estimators are not invariant to the
representation of the binomial data. In the current work, we present a parameter-dependent
adjustment scheme which is applicable to binomial-response generalized linear models with arbitrary
link functions. The adjustment scheme results by the expressions for the bias-reducing
adjusted score functions in Kosmidis & Firth (2008, Biometrika) and thus its use guarantees
estimators with second-order bias. Based on an appropriate expression of the adjusted data,
a procedure for obtaining the bias-reduced estimates is developed which relies on the iterative
adjustment of the binomial responses and totals using existing maximum likelihood implementations.
Furthermore, it is shown that the bias-reduced estimator, like the maximum likelihood
estimator, is invariant to the representation of the binomial data. A complete enumeration
study is used to demonstrate the superior statistical properties of the bias-reduced estimator to
the maximum likelihood estimator
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Jeffreys-prior penalty for high-dimensional logistic regression: A conjecture about aggregate bias
No full textFirth (1993, Biometrika) shows that the maximum Jeffreys\u27 prior penalized likelihood estimator in logistic regression has asymptotic bias decreasing with the square of the number of observations when the number of parameters is fixed, which is an order faster than the typical rate from maximum likelihood. The widespread use of that estimator in applied work is supported by the results in Kosmidis and Firth (2021, Biometrika), who show that it takes finite values, even in cases where the maximum likelihood estimate does not exist. Kosmidis and Firth (2021, Biometrika) also provide empirical evidence that the estimator has good bias properties in high-dimensional settings where the number of parameters grows asymptotically linearly but slower than the number of observations. We design and carry out a large-scale computer experiment covering a wide range of such high-dimensional settings and produce strong empirical evidence for a simple rescaling of the maximum Jeffreys\u27 prior penalized likelihood estimator that delivers high accuracy in signal recovery, in terms of aggregate bias, in the presence of an intercept parameter. The rescaled estimator is effective even in cases where estimates from maximum likelihood and other recently proposed corrective methods based on approximate message passing do not exist
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Empirical bias-reducing adjustments to estimating functions
No full textWe develop a novel and general framework for reduced-bias -estimation from
asymptotically unbiased estimating functions. The framework relies on an
empirical approximation of the bias by a function of derivatives of estimating
function contributions. Reduced-bias -estimation operates either implicitly,
by solving empirically-adjusted estimating equations, or explicitly, by
subtracting the estimated bias from the original -estimates, and applies to
models that are partially- or fully-specified, with either likelihoods or other
surrogate objectives. Automatic differentiation can be used to abstract away
the only algebra required to implement reduced-bias -estimation. As a
result, the bias reduction methods we introduce have markedly broader
applicability with more straightforward implementation and less algebraic or
computational effort than other established bias-reduction methods that require
resampling or evaluation of expectations of products of log-likelihood
derivatives. If -estimation is by maximizing an objective, then there always
exists a bias-reducing penalized objective. That penalized objective relates
closely to information criteria for model selection, and can be further
enhanced with plug-in penalties to deliver reduced-bias -estimates with
extra properties, like finiteness in models for categorical data. The
reduced-bias -estimators have the same asymptotic distribution as the
original -estimators, and, hence, standard procedures for inference and
model selection apply unaltered with the improved estimates. We demonstrate and
assess the properties of reduced-bias -estimation in well-used, prominent
modelling settings of varying complexity
Bias corrected z-tests for regression models
No full textIn regression settings the e ect of a covariate, accounting for all the others, on the dependent variable is typically tested by using a z-statistic. Under regularity conditions on the model and assuming the null hypothesis holds, the associated Wald pivot is asymptotically normally distributed. However, its nite- sample distribution can be far from Gaussian when the sample size is small or moderate relative to the dimension of the global parameter. In this work, asymptotic bias correction of the Wald z-statistic is proposed as a means to improve the accuracy of rst-order inference for the regression coe cients
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
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