Generalized $$C(\alpha )$$ Tests for Estimating Functions with Serial Dependence

2016 ◽  
pp. 151-178
Author(s):  
Jean-Marie Dufour ◽  
Alain Trognon ◽  
Purevdorj Tuvaandorj
Keyword(s):  
Serial Dependence ◽  
Estimating Functions

PG-less Vector Control with Estimating Functions of Motor Parameter.

1991 ◽  
Vol 111 (5) ◽  
pp. 373-378 ◽  
Author(s):  
Naoki Yamamura ◽  
Masahiko Iwasaki ◽  
Hirotaka Sakurai ◽  
Yuzuru Tunehiro
Keyword(s):  
Vector Control ◽  
Estimating Functions ◽  
Motor Parameter

scholarly journals Serial dependence of facial identity reflects high-level face coding

2021 ◽  
Vol 182 ◽  
pp. 9-19
Author(s):  
Kaitlyn Turbett ◽  
Romina Palermo ◽  
Jason Bell ◽  
Dewi Anna Hanran-Smith ◽  
Linda Jeffery
Keyword(s):  
Serial Dependence ◽  
Facial Identity ◽  
High Level

Interspike interval dependency from arterial chemoreceptors

1985 ◽  
Vol 59 (5) ◽  
pp. 1566-1570 ◽  
Author(s):  
D. F. Donnelly ◽  
W. F. Nolan ◽  
E. J. Smith ◽  
R. E. Dutton
Keyword(s):  
Carotid Body ◽  
Interspike Interval ◽  
Correlation Coefficients ◽  
Feedback System ◽  
Serial Dependence ◽  
Control Series ◽  
First Order ◽  
System Operating ◽  
Poisson Type

The carotid body impulse generator has been previously characterized as a Poisson-type random process. We examined the validity of this characterization by analyzing sinus nerve spike trains for interspike interval dependency. Fifteen single chemoreceptive afferents were recorded in vivo under hypoxic-hypercapnic conditions, and approximately 1,000 consecutive interspike intervals for each fiber were timed and analyzed for serial dependence. The same set of intervals placed in shuffled order served as a control series without serial dependence. The original spike interval trains showed significantly negative first-order serial correlation coefficients and less variability in joint interval distributions than did the shuffled interval trains. These results suggest that the chemoreceptor afferent train is not random and may reflect a negative feedback system operating within the carotid body that limits variation about a mean frequency.

Serial dependence of observations leading to contingency tables, and corrections to chi-squared statistics

Biometrika ◽  
1983 ◽  
Vol 70 (1) ◽  
pp. 139-144 ◽  
Author(s):  
SIMON TAVARÉ ◽  
PATRICIA M. E. ALTHAM
Keyword(s):  
Contingency Tables ◽  
Serial Dependence ◽  
Chi Squared

Rank-based tests for autoregressive against bilinear serial dependence

1996 ◽  
Vol 6 (2-3) ◽  
pp. 253-272 ◽  
Author(s):  
Youssef Benghabrit ◽  
Marc Hallin
Keyword(s):  
Serial Dependence

scholarly journals Perceptual decisions are biased toward relevant prior choices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Helen Feigin ◽  
Shira Baror ◽  
Moshe Bar ◽  
Adam Zaidel
Keyword(s):  
Sensory Information ◽  
Irrelevant Stimulus ◽  
Stimulus Feature ◽  
Serial Dependence ◽  
Low Level ◽  
Subsequent Test ◽  
Key Factor ◽  
High Level ◽  
Motor Actions ◽  
The Brain

AbstractPerceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy

2021 ◽  
Vol 12 (4) ◽  
pp. 401-414 ◽  
Author(s):  
Maonatlala Thanwane ◽  
Sandile C. Shongwe ◽  
Muhammad Aslam ◽  
Jean-Claude Malela-Majika ◽  
Mohammed Albassam
Keyword(s):  
Measurement Errors ◽  
Moving Average ◽  
Length Distribution ◽  
Real Life ◽  
Sampling Strategy ◽  
Serial Dependence ◽  
Statistical Efficiency ◽  
Average Scheme ◽  
Run Length Distribution ◽  
Weighted Moving Average

The combined effect of serial dependency and measurement errors is known to negatively affect the statistical efficiency of any monitoring scheme. However, for the recently proposed homogenously weighted moving average (HWMA) scheme, the research that exists concerns independent and identically distributed observations and measurement errors only. Thus, in this paper, the HWMA scheme for monitoring the process mean under the effect of within-sample serial dependence with measurement errors is proposed for both constant and linearly increasing measurement system variance. Monte Carlo simulation is used to evaluate the run-length distribution of the proposed HWMA scheme. A mixed-s&m sampling strategy is incorporated to the HWMA scheme to reduce the negative effect of serial dependence and measurement errors and its performance is compared to the existing Shewhart scheme. An example is given to illustrate how to implement the proposed HWMA scheme for use in real-life applications.

Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence

2010 ◽  
Vol 105 (492) ◽  
pp. 1467-1479 ◽  
Author(s):  
Michael Smith ◽  
Aleksey Min ◽  
Carlos Almeida ◽  
Claudia Czado
Keyword(s):  
Longitudinal Data ◽  
Serial Dependence

Combining estimating functions for volatility

2009 ◽  
Vol 139 (4) ◽  
pp. 1449-1461 ◽  
Author(s):  
M. Ghahramani ◽  
A. Thavaneswaran
Keyword(s):  
Estimating Functions
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