Some Nonparametric Tests for Comovements Between Time Series

Goodman, Leo A.; Grunfeld, Yehuda

doi:10.2307/2282326

Cited by 12 publications

(5 citation statements)

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“…Results are expressed as mean + 1 standard error of the mean (SEM) unless otherwise indicated. To evaluate the relationship between the time courses of the insulin concentration and the glucose concentration a distributionfree statistical analysis has been used [7]. For each patient an index of association between the insulin profile and the glu- cose profile was estimated.…”

Section: Methodsmentioning

confidence: 99%

Overnight metabolic profiles in very young insulin-dependent diabetic children

et al. 1986

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Section: Methodsmentioning

confidence: 99%

Overnight metabolic profiles in very young insulin-dependent diabetic children

et al. 1986

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“…In addition to the lack of relationship in Fig. 4 , there is no significance ( p = .19, two sided) in a Goodman–Grunfeld ( 1961 ) time series test for co-movement. The chickenpox data show that changes in measles incidence are not reporting artifacts.…”

Section: Resultsmentioning

confidence: 85%

A ‘post-honeymoon’ measles epidemic in Burundi: mathematical model-based analysis and implications for vaccination timing

Corey

Noymer

2016

PeerJ

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Using a mathematical model with realistic demography, we analyze a large outbreak of measles in Muyinga sector in rural Burundi in 1988–1989. We generate simulated epidemic curves and age × time epidemic surfaces, which we qualitatively and quantitatively compare with the data. Our findings suggest that supplementary immunization activities (SIAs) should be used in places where routine vaccination cannot keep up with the increasing numbers of susceptible individuals resulting from population growth or from logistical problems such as cold chain maintenance. We use the model to characterize the relationship between SIA frequency and SIA age range necessary to suppress measles outbreaks. If SIAs are less frequent, they must expand their target age range.

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“…To assess how much the lines in Fig. 2 are moving in parallel, we performed Goodman–Grunfeld (G–G) tests for co-movement of time series with correction for serial correlation ( Goodman & Grunfeld, 1961 ). Co-movement in this statistical test refers to correlations of signs of first differences of two series.…”

Section: Resultsmentioning

confidence: 99%

Did the 1918 influenza cause the twentieth century cardiovascular mortality epidemic in the United States?

Tate

Namkung

Noymer

2016

PeerJ

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During most of the twentieth century, cardiovascular mortality increased in the United States while other causes of death declined. By 1958, the age-standardized death rate (ASDR) for cardiovascular causes for females was 1.84 times that for all other causes, combined (and, for males, 1.79×). Although contemporary observers believed that cardiovascular mortality would remain high, the late 1950s and early 1960s turned out to be the peak of a roughly 70-year epidemic. By 1988 for females (1986 for males), a spectacular decline had occurred, wherein the ASDR for cardiovascular causes was less than that for other causes combined. We discuss this phenomenon from a demographic point of view. We also test a hypothesis from the literature, that the 1918 influenza pandemic caused the cardiovascular mortality epidemic; we fail to find support.

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Some Nonparametric Tests for Comovements Between Time Series

Cited by 12 publications

References 0 publications

Overnight metabolic profiles in very young insulin-dependent diabetic children

Overnight metabolic profiles in very young insulin-dependent diabetic children

A ‘post-honeymoon’ measles epidemic in Burundi: mathematical model-based analysis and implications for vaccination timing

Did the 1918 influenza cause the twentieth century cardiovascular mortality epidemic in the United States?

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