bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factor Analysis, Overview
Authors: Jolliffe Ian T. 
Published: July 2005

AbstractFactor analysis is a latent variable technique. It reduces the dimension of a data set by assuming that underlying the measured variables is a smaller number of unobservable factors. The measured variables are assumed to be linearly related to the factors, apart from an error term. There are many ways of estimating the loadings that relate the variables to the factors, and predicting the scores of the factors themselves. Solutions are typically nonunique, and are made as “simple” as possible by means of rotation of loadings. Much of factor analysis has been developed within psychology, but it is useful more widely. The technique is often used in an exploratory or descriptive manner, but confirmatory versions are also available.
Multivariate Classification Rules: Calibration and Discrimination
Authors: Schmid Christopher H. , Griffith John L. 
Published: July 2005

AbstractThis article presents a number of measures of discrimination and calibration, along with graphical representations of calibration and discrimination assessment. It emphasizes multivariate classification rules for models, where the classification is into one of two possible states, and also discusses extensions to multistate classifications. The c‐index and the Hosmer–Lemeshow χ2statistic are the most widely used measures of discrimination and calibration.
Orthoblique Rotation
Authors: Jackson J. Edward 
Published: July 2005

Abstract

Orthoblique rotation
is a two‐stage procedure for obtaining
oblique rotations
used to transform vectors associated with
principal component analysis
or
factor analysis
to
simple structure
. The first stage will usually be an orthogonal rotation such as
Varimax
. The nature of the second stage will depend to some extent on the degree of obliqueness desired. A numerical example is included with two second‐stage options. This technique is sometimes referred to as the
Harris–Kaiser rotation
.
Primary Factors
Authors: D'Agostino Ralph B. , Russell Heidy K. 
Published: July 2005

Abstract
One of the aims of factor analysis is to display the configuration of variables in as simple a manner as possible. There are several techniques for achieving this. Primary axes are the factor axes that are rotated to a good fit to the configurations of the variables. They are the unit vectors that lie in the intersection of the primary factors, which represent the ideal variables, each of which measures only one common factor with no unique factors. Reference vectors are defined as the axes that are perpendicular to the primary factors. Various techniques are illustrated for obtaining projections of variable vectors onto the different sets of axes. These are extremely important for interpretation of results.
Algorithm
Authors: Maindonald John H. 
Published: July 2005

AbstractThe term algorithm is generally used to describe a series of logical steps that, when implemented within a computer program, will perform a desired computational task. The task must be defined precisely. Issues that are generally important in finding or choosing such an algorithm include: computational cost, computer memory or storage requirements, accuracy and precision, simplicity of description, and the tuning of the algorithm to computer architecture and organization.
Bootstrap Method
Authors: De Angelis D. , Young G. A. 
Published: July 2005

Abstract
Bootstrap methods are procedures for the empirical estimation or approximation of sampling distributions and their characteristics. Their primary use lies in the estimation of accuracy measures, such as bias and variance, for parameter estimators, and in construction of confidence sets or hypothesis tests for population parameters. They are applied in circumstances in which the form of the population from which the observed data have been drawn is unknown. They prove particularly useful where very limited sample data are available and traditional parametric modeling and analysis are difficult or unreliable.
Computer‐Assisted Interviewing
Authors: Brinkley Eden , Finlay Elizabeth , Wensing Fred 
Published: July 2005

Abstract
Computer‐assisted interviewing (CAI) refers to the use of a computer to aid the interview process in survey data collection. The computer generally presents the question text on the screen, along with allowable response categories, and the interviewer or respondent records the answers directly into the computer. The computer can also be used to undertake various forms of processing at the time of the interview, and can facilitate electronic data transfer. The advantages and disadvantages of CAI relative to paper and pencil interviewing (PAPI) are discussed briefly.
Chi‐Square Distribution
Authors: Lancaster H. O. , Seneta E. 
Published: July 2005

Abstract
A chi‐square random variable is defined as the sum of squares of independently distributed standard normal random variables, which explains the additive property of independent chi‐square random variables. Its probability distribution is described by a gamma probability density. The chi‐square goodness‐of‐fit statistic, when sample size is large, is approximately a chi‐square random variable. Tests of hypotheses relating to contingency tables are also based on a statistic with approximate chi‐square distribution.
Estimation
Authors: Doksum Kjell A. 
Published: July 2005

Abstract
One of the principal objects of a statistical analysis is to estimate the value of a parameter from observed data. This article is concerned mainly with point (as distinct from interval) estimation. Methods described include the frequency plug‐in method, the method of moments, weighted and generalized least squares, maximum likelihood, and Bayesian estimation. Attention is given to the properties of unbiasedness and efficiency.
Identifiability
Authors: Greenland Sander 
Published: July 2005

Abstract

Consider a vector
Y
of random variables having a distribution
F
(
y;θ
) that depends on an unknown parameter vector
θ
.
θ
is
identifiable
by observation of
Y
if distinct values
θ
1
and
θ
2
for
θ
yield distinct distributions for
Y
, that is, if
θ
1
≠
θ
2
implies
F
(
y
;
θ
2
) for some
y
. A function
g
(
θ
) is
identifiable
by observation of
Y
if
g
(
θ
1
) ≠
g
(
θ
2
) implies
F
(
y
;
θ
1
) ≠
F
(
y
;
θ
2
) for some
y
. Note that
θ
is identifiable if and only if all functions of
θ
are identifiable.
Likelihood
Authors: Hills Michael 
Published: July 2005

Abstract
Likelihood is the probability (or probability density) of the observed data, given a probability model, regarded as a function of the unknown parameters. It needs to be defined only up to a constant of proportionality, because ratios of likelihoods are used to compare different parameter values. It is used extensively as the basis for procedures of frequentist inference, such as maximum likelihood estimation, score tests and likelihood ratio tests, and is central to Bayesian inference. Some systems of inference are based on likelihood alone.
Prior Distribution
Authors: Kass R. E. 
Published: July 2005

Abstract
In Bayesian inference, the unknown parameter is given a prior distribution, and Bayes' theorem combines this with the likelihood from the observed data, to give the posterior distribution. The prior may be informative, subjective, or have a standard, reference form (usually representing vague knowledge). In hierarchical models, the prior distribution may have its own parameters that themselves have priors.
R
adon–
N
ikodym Theorem
Authors: Alexander Kenneth S. 
Published: July 2005

Abstract
The Radon–Nikodym Theorem, in the context of probability measures, states that if all events having probability 0 under one probability measure also have probability 0 under a second measure, then the second measure is obtained by integrating some density function with respect to the first measure. Applications in probability and statistics arise in the contexts of likelihood functions, conditional expectation, Girsanov's formula, spectral measures and local time. The theorem and these applications are discussed.
Rank Correlation
Authors: Kraemer Helena Chmura 
Published: July 2005

Abstract
When observations on two variables are made on each of a number of units, the association between the variables is often measured by the Pearson product‐moment correlation coefficient. This is inappropriate if the variables are ordinal or when their distributions are far from normal. Rank correlation coefficients are based on the ranks of the observations. Spearman's coefficient is obtained by using the ranks in the usual formula for the product‐moment coefficient. Kendall's tau coefficient is expressed through comparisons between all pairs of units, noting, for each pair, whether the two variables order the units in the same or the opposite direction. Methods of inference are outlined.
Robustness
Authors: Prescott Philip 
Published: July 2005

AbstractA short history of the development of ideas of robustness is provided, from Gauss' introduction of the normal distribution in 1821 to the detailed examination of the conflicting criteria for robustness as discussed by Huber, Hampel, Tukey, and others, which led to consideration of validity robust and efficiency robust procedures. Robust estimators of location using M‐estimators (based on maximum likelihood), L‐estimators (based on order statistics), and R‐estimators (based on ranking methods) are described and examples given. These methods produce a wide range of robust estimators including trimmed means, Winsorized means, and the median of all pairwise averages. Adaptive and resistant procedures are discussed briefly.
Sign Tests
Authors: Dietz E. Jacquelin 
Published: July 2005

Abstract
The sign test is a simple distribution‐free test that can be applied easily in a variety of situations. Observations, which may be difference scores, are replaced by their positive or negative signs and analyzed using a binomial test. The specific applications discussed here are a location test for the median, the Cox–Stuart test for tend, and the McNemar test for correlated proportions.
Weighted Distributions
Authors: Patil G. P. 
Published: July 2005

Abstract
Traditional environmetric theory and practice have been occupied largely with randomization and replication. But in environmental and ecological work, observations also fall in the nonexperimental, nonreplicated, and nonrandom categories. The problems of model specification and data interpretation then acquire special importance and great concern. The theory of weighted distributions provides a unifying approach for these problems. Weighted distributions take into account the method of ascertainment, by adjusting the probabilities of actual occurrence of events, to arrive at a specification of the probabilities of those events as observed and recorded. Failure to make such adjustments can lead to incorrect conclusions.
Magic Square Designs
Authors: Martin Richard J. 
Published: July 2005

Abstract

An
n
by
n
magic square with entries 1 to n
∧
2 has all row and column sums equal. If
n
has
k
factors, then a factorial design with n
∧
2 runs and up to 2
k
factors can be obtained from this for which the main effects and at least some of the two‐factor interactions are linear‐trend‐free (their estimates are unaffected if there is a straight line trend over time). An unconnected experimental design called a
magic Latin square
is a Latin square with additional blocking obtained from structured rows and columns.
A
merican Public Health Association
Authors: Feinleib Manning 
Published: July 2005

Abstract

The
American Public Health Association
(APHA) is the oldest and largest organization of public health professionals in the world, with more than 50 000 members in over 50 occupations of public health in 2004. APHA covers issues affecting personal and environmental health, including funding for health programs, pollution control, programs and policies related to chronic and infectious diseases, a smoke‐free society, and professional education in public health. APHA's activities include scientific and practice programs, annual meetings, and an awards program; it also publishes the
American Journal of Public Health
and
The Nation's Health
.
B
ernard,
C
laude
Authors: Fagot‐Largeault A. 
Published: July 2005

Abstract
A young man of a very modest extraction (his parents were vineyard workers), Claude Bernard received a pious and humanistic education, and started working as an assistant pharmacist. At 21 he ventured to Paris with the ambition of making a career in literature. He was advised rather to learn a job that would enable him to earn a living. He then turned to medicine, which he studied conscientiously, although clinical work did not much appeal to him. As a resident at the Hotel‐Dieu, under François Magendie, he realized what he really wanted to do: experimental work in physiology. For four years he assisted Magendie in his laboratory at the Collège de France, and became an expert at using animal vivisection to trace physiologic facts.
C
halmers,
T
homas
C
lark
Authors: Wright E. 
Published: July 2005

Abstract
Born in 1917 in Forest Hills, NY; died in 1995 in Hanover, NH. Chalmers was a leader in the design, conduct, and evaluation of clinical trials, favoring randomized controlled trials in all areas of medicine, with independent policy advisory committees. He helped found the Society for Clinical Trials.
C
ox,
G
ertrude
M
ary
Authors: Anderson R. L. 
Published: July 2005

Abstract
Gertrude Mary Cox was one of the twentieth century pioneers in Statistics. After receiving her BS and MS degrees in Mathematics and Statistics from Iowa State, she took charge of the computing and data analysis in its Statistical Laboratory. She developed and taught classes in Design of Experiments, using real data.
In 1940, she accepted the headship of the newly established Department of Experimental Statistics at North Carolina State. She started staffing with statisticians who had strong programs in applied fields. In 1944, she became the head of an all‐university Institute of Statistics.
In 1949, Gertrude Cox gave up the Headship at NCS to help develop Statistics at the Research Triangle Park in North Carolina; Dr. Cox became RTI's first Director of Statistics Research in 1959. She had received an honorary doctor's degree from NC State in 1958. Her most treasured honor came in 1975, when she was elected to the National Academy of Science.

Dr. Cox received much outside support; a special one helped establish an outstanding statistical genetics program and the development of a strong computer program. She was a founding member of the International Biometrics Society and became President and Editor of its journal,
Biometrics
. She also became President of the American Statistical Association and consulted with many federal organizations. The building housing Statistics at NCS was named Cox Hall in 1977.

Unfortunately, Gertrude contracted leukemia and died in Duke Hospital in 1978.
F
arr,
W
illiam
Authors: Asmar Lina 
Published: July 2005

Abstract
William Farr was born November 30, 1807 in Shropshire, UK, and must be counted as the founder of epidemiology in its modern form. He studied many aspects of the distribution and determinants of health disorders in populations, and the application of the studies to the prevention and control of disease. His publications have been grouped under six headings: population, marriages, births, deaths, life tables, and miscellaneous. He developed an interest in accurate mortality statistics by age and sex and changed the British system of death registration into an instrument for measuring the sanitary condition of the country. His studies on mortality differences among different occupations helped in understanding industrial hazards. William Farr died April 14, 1883, UK.
Indian Statistical Institute
Authors: Rao S. B. 
Published: July 2005

Abstract

The Institute was founded in 1932, with P.C. Mahalanobis as Honorary Secretary, and now has about 500 scientific staff, with headquarters in Calcutta. The journal
Sankhyā
was started in 1933. Work covered many areas of statistical theory and practice, especially in multivariate analysis, sample surveys, experimental design, and quality control. Important agricultural and socioeconomic surveys were carried out. The Institute has an extensive undergraduate and graduate teaching program.
Medical Journals, Statistical Articles in
Authors: Johnson Anthony L. , Altman Douglas G. 
Published: July 2005

AbstractThere are thousands of publications that cover aspects of statistics scattered throughout the vast biomedical literature. This article provides a broad overview by categorizing them under seven headings: isolated papers on a particular statistical issue; series of thematic papers dedicated to a narrow statistical area; series of papers covering broad areas of medical statistics; guidelines; surveys of published papers reporting frequency of usage of statistical techniques; reviews of published papers examining critically, aspects of design, analysis, conduct, presentation, and summary; and finally, systematic reviews (meta‐analysis) incorporating assessment of methodologic quality. Each category is described and discussed, and illustrated by examples selected to provide an idea of their scope, diversity, and quality
M
edical
R
esearch
C
ouncil (
MRC
)
Authors: Austoker Joan , Altman Douglas G. 
Published: July 2005

Abstract
The Medical Research Council of the United Kingdom has supported statistical work since 1914, primarily through its Statistical Research Unit (now Biostatistics Unit) but also in many other Units. It has been associated with important research in the methodology and practice of clinical trials and epidemiology.
P
opper,
K
arl
R
Authors: Fagot‐Largeault A. 
Published: July 2005

Abstract

Born 1902; died 1994. Famous as a philosopher of science, Popper maintained that scientific truth is not manifest, but that indeed that scientific theories are conjectures open to refutation or falsification. Popper controversially denounced inductive reasoning—validity of a scientific theory from empirical observations to general regularities—insisting instead that scientific theories should be open to testing. As a theory passes more severe tests, it becomes more highly “corroborated”. He called this approach
hypothetico‐deductive
. ‘Realism and the Aim of Science’ (1982) is his work most relevant to biostatistics, re‐examining in detail problems of truth, corroboration, and probability.
S
avage,
L
eonard
J
immie
Authors: Lindley Dennis V. 
Published: July 2005

Abstract
L.J. Savage (1917–1971) is best known for his book on the foundations of statistics in which he rigorously developed a statistical method for decision‐making based on the classic ideas of pure mathematics, developing theorems from axioms. This later came to be termed the Bayesian paradigm for inference and for decision‐making. His argument also introduced the concept of utility and the principle of maximization of expected utility.
Statistical Review for Medical Journals, Journal's Perspective
Authors: Smith Richard 
Published: July 2005

AbstractMedical journals have moved away from case reports toward accounts of research studies, particularly randomized trials and epidemiologic studies, and statisticians have become central to medical journals and medical practice. They have played an important part in the move from opinion to evidence‐based medicine. The author is Editor of theBritish Medical Journaland describes the contributions of statisticians to that journal's reviewing process: they are included in the team taking the final decision on whether to publish a paper. The article contains recommendations for policy on statistical review.
Statisticians in the Pharmaceutical Industry (
PSI
)
Authors: Morgan David , Gordon Kerry 
Published: July 2005

Abstract
PSI (Statisticians in the Pharmaceutical Industry Limited) is a nonprofit organization, primarily UK‐based. PSI is open to all people interested in the application of statistics in the pharmaceutical industry, aiming to promote professional standards, foster discussion, and influence regulatory direction and scientific methodologies in drug development. Members work in all areas of the drug development process, including research, preclinical and clinical development, production, quality control, marketing, and market research.
UniversityGroupDiabetesProgram (UGDP)
Authors: Knatterud Genell L. 
Published: July 2005

AbstractThe University Group Diabetes Program (UGDP) was one of the first large‐scale cooperative clinical trials designed and implemented in the United States. The three objectives of the program were (1) evaluation of the effects on the development of vascular complications in adult‐onset noninsulin dependent diabetes of the different hypoglycemic treatments accepted in clinical practice in late 1950s and early 1960s (diet, insulin, and oral agents); (2) study of the natural history of vascular disease in adult‐onset diabetics; and (3) development of methods appropriate for the design and conduct of cooperative clinical trials.The study provided little evidence that insulin treatment was any better than diet alone in altering the course of vascular complications in stable adult‐onset diabetes. This was true whether insulin was given in a fixed dose or adjusted to maintain blood glucose within certain levels.Tolbutamide therapy was discontinued prior to the end of follow‐up for patients in the diet and insulin groups, when it became apparent that there was an excess cardiovascular mortality for patients in the tolbutamide treatment group compared to patients assigned to placebo or diet alone. About two years after the decision was made concerning tolbutamide, patients assigned to phenformin treatment were also discontinued when the observed mortality for all causes and from cardiovascular causes was higher than mortality in any of the other treatment groups.Since the last UGDP major report was published in 1992, there has been little evidence provided to indicate benefit of therapy for the prevention of macrovascular diabetic complications in adult‐onset diabetes. The methods and procedures used in the UGDP Coordinating Center provided a framework for standards of operations for future Coordinating Centers of multicenter clinical trials.
World Health Organization (
WHO
): Global Health Situation
Authors: Hapsara H. R. 
Published: July 2005

Abstract

WHO has responsibility for the global epidemiologic surveillance of disease, receiving information on outbreaks of communicable disease and distributing this information throughout the world. WHO is also responsible for the
International Health Regulations
, the International Classification of Diseases, and other standards, which together make international epidemiologic comparisons possible. WHO also publishes
World Health Statistics Quarterly
,
World Health Statistics Annual
,
World Health Report
. The thirtieth World Health Assembly decided that the main social target of governments and WHO in the coming decades should be “health for all” by the year 2000. Member states committed themselves to monitoring progress toward, and evaluating the attainment of, this common goal. The health‐for‐all monitoring and evaluation process is intended to establish a baseline of current health and socioeconomic conditions, against which progress toward defined targets and objectives can be measured. Periodic measurement should establish trends that will permit anticipation of future conditions, and enable planning for them. The Health Situation and Trend Assessment Program is at present responsible for global health situation analysis and projection; strengthening of country health information; and partnerships and coordination of epidemiology, statistics, and trend assessment. Global epidemiologic surveillance is under the responsibility of the Emerging and other Communicable Diseases Surveillance and Control Program