11 research outputs found
Alternative parameter learning schemes for monitoring process stability
In statistical process control, accurately estimating in-control (IC) parameters is crucial for effective monitoring. This typically requires a Phase I analysis to obtain estimates before monitoring commences. The traditional “fixed” estimate (FE) approach uses these estimates exclusively, while the “adaptive” estimate (AE) approach updates the estimates with each new observation. Such extreme criteria reflect the traditional bias-variance tradeoff in the framework of the sequential parameter learning schemes. This paper proposes an intermediate update rule that generalizes two ad hoc criteria for monitoring univariate Gaussian data, by giving a lower probability to parameter updates when an out-of-control (OC) situation is likely, therefore updating more frequently when there is no evidence of an OC scenario. The simulation study shows that this approach improves the detection power for small and early shifts, which are commonly regarded as a weakness of control charts based on fully online adaptive estimation. The paper also shows that the proposed method performs similarly to the fully adaptive procedure for larger or later shifts. The proposed method is illustrated by monitoring the sudden increase in ICU counts during the 2020 COVID outbreak in New York.</p
Canonical analysis of relative production) in cattle production in Brazil (N–North, NE–Northeast, SE–Southeast, S–South, and MW–Midwest).
<p>(Can 1 and Can 2 are the first tow canonical scores for each municipality). Each point on the graph represents a municipality, with those that are named showing highest discriminatory values.</p
Cluster of Brazilian municipalities according to the acceleration of growth in the number of cattle (Standard error) and the percentage of the total herd over the 5-year period.
<p>Cluster of Brazilian municipalities according to the acceleration of growth in the number of cattle (Standard error) and the percentage of the total herd over the 5-year period.</p
Midpoint of cattle production in Brazil by year.
<p>Midpoint of cattle production in Brazil by year.</p
Growth of cattle production in Brazil by municipality by period: (A) 1977–1981; (B) 1982–1986; (C) 1987–1991; (D) 1992–1996; (E) 1997–2001; (F) 2002–2006; and (G) 2007–2011.
<p>Growth of cattle production in Brazil by municipality by period: (A) 1977–1981; (B) 1982–1986; (C) 1987–1991; (D) 1992–1996; (E) 1997–2001; (F) 2002–2006; and (G) 2007–2011.</p
Clusters of Brazilian municipalities according to the relative growth in the number of cattle (standard error) and the percentage of the total herd from 1977 to 2011 in 5-year periods.
<p>Clusters of Brazilian municipalities according to the relative growth in the number of cattle (standard error) and the percentage of the total herd from 1977 to 2011 in 5-year periods.</p
Total cattle numbers (periods of 5 years) in cattle production in Brazil (N–North, NE–Northeast, SE–Southeast, S–South, and MW–Midwest).
<p>Total cattle numbers (periods of 5 years) in cattle production in Brazil (N–North, NE–Northeast, SE–Southeast, S–South, and MW–Midwest).</p
Relative growth of cattle numbers and percentage (standard error) in Brazil from 1977 to 2011 in 5-year periods by region.
<p>Relative growth of cattle numbers and percentage (standard error) in Brazil from 1977 to 2011 in 5-year periods by region.</p
Acceleration of Cattle Production in Brazil by period: (A) 1977–1986; (B) 1982–1991; (C) 1987–1996; (D) 1992–2001; (E) 1997–2006; and (F) 2002–2011.
<p>Acceleration of Cattle Production in Brazil by period: (A) 1977–1986; (B) 1982–1991; (C) 1987–1996; (D) 1992–2001; (E) 1997–2006; and (F) 2002–2011.</p
Canonical analysis of acceleration in cattle production in Brazil (N–North, NE–Northeast, SE–Southeast, S–South, and MW–Midwest).
<p>(Can 1 and Can 2 are the first tow canonical scores for each municipality). Each point on the graph represents a municipality, with those that are named showing highest discriminatory values.</p