An F-type test for detecting departure from monotonicity in a functional linear model

Montoya, Eduardo L.; Meiring, Wendy

doi:10.1080/10485252.2016.1163352

Cited by 1 publication

(1 citation statement)

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“…The discretized estimates of the warping functions are represented as functions in a similar manner but the B-spline basis coefficients are monotonically constrained to ensure that the functional representation of the warping functions are monotonically increasing. One may refer to Montoya and Meiring (2016) and others cited in their paper for details on monotonically constrained B-splines. The left, middle, and right panels of Figure 4 show the PM10 curves, the estimated warping functions (h i j k l (t)'s), and the corresponding registered curves (pm * i j k l (t)'s) for all (i, j, k, l).…”

Section: Separating the Height And Time Variation Of Pm10mentioning

confidence: 99%

Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data

Montoya

2018

CSAM

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Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

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Section: Separating the Height And Time Variation Of Pm10mentioning

confidence: 99%