A comparison of confidence region estimators for multivariate simulation output

In this paper, we reconstruct the late-time cosmological dynamics using a purely kinematic approach. In particular, considering a divergence-free parametrization for deceleration parameter [Formula: see text], we first derive the jerk parameter [Formula: see text] and then confront it with combination of various cosmological datasets. We use the most recent observational datasets consisting of the 1048 Pantheon Supernovae Ia data points in the redshift range [Formula: see text], the 51 data points of observational Hubble parameter (OHD) measurements in the redshift range [Formula: see text], the Hubble constant [Formula: see text] (R19) and the CMB shift parameter measurements. We study the evolution of different cosmological quantities for the present model and compare it with the concordance [Formula: see text]CDM model. We find that only the combined Pantheon+OHD+R19 data shows good agreement with the [Formula: see text]CDM [Formula: see text] model within [Formula: see text] confidence region. We also find that our model successfully generates late time cosmic acceleration along with a decelerated expansion in the past.

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A Confidence Region for Zero-Gradient Solutions for Robust Parameter Design Experiments

International Journal of Quality Statistics and Reliability ◽

10.1155/2011/537543 ◽

2011 ◽

Vol 2011 ◽

pp. 1-11

Author(s):

Aili Cheng ◽

John Peterson ◽

Pallavi Chitturi

Keyword(s):

Confidence Region ◽

Control Variable ◽

Critical Values ◽

Parameter Design ◽

Feasible Region ◽

Robust Parameter Design ◽

Control Variables ◽

Controllable Factors ◽

Key Issues ◽

Robust Parameter

One of the key issues in robust parameter design is to configure the controllable factors to minimize the variance due to noise variables. However, it can sometimes happen that the number of control variables is greater than the number of noise variables. When this occurs, two important situations arise. One is that the variance due to noise variables can be brought down to zero The second is that multiple optimal control variable settings become available to the experimenter. A simultaneous confidence region for such a locus of points not only provides a region of uncertainty about such a solution, but also provides a statistical test of whether or not such points lie within the region of experimentation or a feasible region of operation. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage.

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