David C Hoaglin
Dados Biográficos
| ID | 4349803 |
|---|---|
| NOME | David C Hoaglin |
| PRENOMES | David C |
| SOBRENOME | Hoaglin |
| ASSINATURA | HOAGLIN D C |
| AFILIAÇÕES | Harvard University |
| ORCID | 0000-0003-1336-181X |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 6 |
| TOTAL DE CITAÇÕES | 1 |
| TOTAL COMO AUTOR | 6 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 1986 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2015 |
| ÍNDICE H | 1 |
Exploratory Data Analysis
Can a Survey Influence Quality of Care in Nursing Homes
Data from a standardized administrative form, the Patient Review Instrument, were used to evaluate whether the New York Quality Assurance System (NYQAS) had an impact on deterioration in functional status or on the incidence of adverse outcomes among residents in New York's nursing homes. The NYQUAS approach evaluated nursing homes by using "triggers" suggestive of deficient quality of care. A random sample of nursing home facilities was selected…
A Statistical Model
Contents: Biography.- Bibliography.- Contributions as a Scientific Generalist.- Contributions to Mathematical Statistics.- Contributions to Methodology and Applications.- Fred as Educator.- Fred at Harvard.- Reviews of Book Contributions
Resistant cross‐ age smoothing of age‐specific percentiles for growth reference data
Resistant delineation, a technique adapted from exploratory data analysis (Tukey, Exploratory Data Analysis, 1977), was applied to smooth age‐specific percentiles for triceps skinfold thickness across ages from 1 to 20 years. Row percentiles were transformed to logarithms to promote symmetry and to render variability more nearly homogeneous across ages. The delineation involved smoothing resistantly the sequences of age‐specific log medians and t…
Fine-Tuning Some Resistant Rules for Outlier Labeling
A previous study examined the performance of a standard rule from Exploratory Data Analysis, which uses the sample fourths, FL and FU , and labels as “outside” any observations below FL – k(FU – FL ) or above FU + k(FU – FL ), customarily with k = 1.5. In terms of the order statistics X (1) ≤ X (2) ≤ X (n) the standard definition of the fourths is FL = X(f) and FU = X (n + 1 − f), where f = 1⁄2[(n + 3)/2] and [·] denotes the greatest-integer func…
Performance of Some Resistant Rules for Outlier Labeling
The techniques of exploratory data analysis include a resistant rule for identifying possible outliers in univariate data. Using the lower and upper fourths, FL and FU (approximate quartiles), it labels as “outside” any observations below FL − 1.5(FU — FL ) or above FU + 1.5(FU — FL ). For example, in the ordered sample −5, −2, 0, 1, 8, FL = −2 and FU = 1, so any observation below −6.5 or above 5.5 is outside. Thus the rule labels 8 as outside. S…
Can a Survey Influence Quality of Care in Nursing Homes
Data from a standardized administrative form, the Patient Review Instrument, were used to evaluate whether the New York Quality Assurance System (NYQAS) had an impact on deterioration in functional status or on the incidence of adverse outcomes among residents in New York's nursing homes. The NYQUAS approach evaluated nursing homes by using "triggers" suggestive of deficient quality of care. A random sample of nursing home facilities was selected…
Performance of Some Resistant Rules for Outlier Labeling
The techniques of exploratory data analysis include a resistant rule for identifying possible outliers in univariate data. Using the lower and upper fourths, FL and FU (approximate quartiles), it labels as “outside” any observations below FL − 1.5(FU — FL ) or above FU + 1.5(FU — FL ). For example, in the ordered sample −5, −2, 0, 1, 8, FL = −2 and FU = 1, so any observation below −6.5 or above 5.5 is outside. Thus the rule labels 8 as outside. S…
Fine-Tuning Some Resistant Rules for Outlier Labeling
A previous study examined the performance of a standard rule from Exploratory Data Analysis, which uses the sample fourths, FL and FU , and labels as “outside” any observations below FL – k(FU – FL ) or above FU + k(FU – FL ), customarily with k = 1.5. In terms of the order statistics X (1) ≤ X (2) ≤ X (n) the standard definition of the fourths is FL = X(f) and FU = X (n + 1 − f), where f = 1⁄2[(n + 3)/2] and [·] denotes the greatest-integer func…
Resistant cross‐ age smoothing of age‐specific percentiles for growth reference data
Resistant delineation, a technique adapted from exploratory data analysis (Tukey, Exploratory Data Analysis, 1977), was applied to smooth age‐specific percentiles for triceps skinfold thickness across ages from 1 to 20 years. Row percentiles were transformed to logarithms to promote symmetry and to render variability more nearly homogeneous across ages. The delineation involved smoothing resistantly the sequences of age‐specific log medians and t…
A Statistical Model
Contents: Biography.- Bibliography.- Contributions as a Scientific Generalist.- Contributions to Mathematical Statistics.- Contributions to Methodology and Applications.- Fred as Educator.- Fred at Harvard.- Reviews of Book Contributions
Can a Survey Influence Quality of Care in Nursing Homes
Data from a standardized administrative form, the Patient Review Instrument, were used to evaluate whether the New York Quality Assurance System (NYQAS) had an impact on deterioration in functional status or on the incidence of adverse outcomes among residents in New York's nursing homes. The NYQUAS approach evaluated nursing homes by using "triggers" suggestive of deficient quality of care. A random sample of nursing home facilities was selected…
Exploratory Data Analysis
Mathematics (5 obras) · Statistics (5 obras) · Advanced Statistical Methods and Models (4 obras) · Computer Science (4 obras) · Advanced Statistical Process Monitoring (2 obras) · Combinatorics (2 obras) · Mathematical analysis (2 obras) · Outlier (2 obras) · Activities of daily living (1 obras) · Algorithm (1 obras)