Cumulative Distribution Approximation of Signal Envelope in Radio Channels with Arbitrary Scattering

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

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Feminisation of Multidimensional Poverty in Rural Odisha

Rupkatha Journal on Interdisciplinary Studies in Humanities ◽

10.21659/rupkatha.v12n5.rioc1s12n2 ◽

2020 ◽

Author(s):

Surya Narayan Biswal ◽

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S. K. Mishra ◽

M. K. Sarangi ◽

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...

Keyword(s):

Rural Areas ◽

Social Groups ◽

Multidimensional Poverty ◽

Cumulative Distribution ◽

Policy Framework ◽

Living Standard ◽

Individual Level ◽

Multidimensional Poverty Index ◽

Order Stochastic Dominance ◽

The Individual

UNDP’s 2030 agenda of Sustainable Development Goals (SDGs) emphasized gender equality in augmenting human capital and alleviating poverty. For eradication of extreme poverty and building resilience for persons who are vulnerable to poverty, SDGs calls for a pro-poor and gender-sensitive policy framework. In this context, a gender-based study on multi-dimensional aspects of poverty is highly significant. Extant literature reveals that females are more deprived in different dimensions of poverty such as education, health, living standard, empowerment, environment, autonomy and social relationship. The present study is conducted with the basic objective of examining feminization of poverty in rural areas of Jagatsinghapur district of Odisha. Seven socio-economic dimensions comprising sixteen indicators have been taken into consideration to construct the Multidimensional Poverty Index (MPI) using the Alkire-Foster (AF) Method at the individual level. The novelty of the study lies in analyzing MPI at the individual level for rural Odisha. Higher female deprivation is observed across social groups and all occupation categories except services. Dummy variable regression analysis also supports the major findings of the study. Complementary Cumulative Distribution Function satisfies strict first-order stochastic dominance condition and substantiates the feminisation of poverty at each level of poverty cut-off across all social groups and occupational categories except for services. The findings of the study have significant implications for developing suitable policies for gender equalization and poverty alleviation.

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Bootstrap Analysis

10.1093/oso/9780198505044.003.0004 ◽

2017 ◽

Author(s):

Russell Cheng

Keyword(s):

Goodness Of Fit ◽

Null Distribution ◽

Real Data ◽

Confidence Regions ◽

Confidence Bands ◽

Cumulative Distribution ◽

Attractive Alternative ◽

Bootstrap Analysis ◽

Parametric Bootstrapping ◽

Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

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