Statisticshttp://hdl.handle.net/10311/942021-01-16T03:58:29Z2021-01-16T03:58:29ZDistribution of geometrically weighted sum of bernoulli ramdom variablesKgosi, PhazamileBhati, DeepeshRattihalli, Ranganath Narayanacharyahttp://hdl.handle.net/10311/18922019-02-16T00:01:54Z2011-01-01T00:00:00ZDistribution of geometrically weighted sum of bernoulli ramdom variables
Kgosi, Phazamile; Bhati, Deepesh; Rattihalli, Ranganath Narayanacharya
A new class of distributions over (0,1) is obtained by considering geometrically weighted sum of independent identically distributed (i.i.d.) Bernoulli random variables. An expression for the distribution function (d.f.)
is derived and some properties are established. This class of distributions includes U(0,1) distribution.
2011-01-01T00:00:00ZManpower systems operating under heavy and light tailed inter-exit time distributionsSivasamy, R.Rao, P. TirupathiThaga, K.http://hdl.handle.net/10311/18912019-02-16T00:01:53Z2014-01-01T00:00:00ZManpower systems operating under heavy and light tailed inter-exit time distributions
Sivasamy, R.; Rao, P. Tirupathi; Thaga, K.
This paper considers a Manpower system where “exits” of employed personnel produce some wastage or loss. This system monitors these wastages over the sequence of exit epochs {t0 = 0 and tk; k = 1, 2,∙∙∙} that form a re- current process and admit recruitment when the cumulative loss of man hours crosses a threshold level Y, which is also called the breakdown level. It is assumed that the inter-exit times Tk = tk−1 − tk, k = 1, 2,∙∙∙ are independent and identically distributed random variables with a common cumulative distribution function (CDF) B(t) = P(Tk < t) which has a tail 1 – B(t) behaving like t−v with 1 < v < 2 as t → ∞. The amounts {Xk} of wastages incurred during these inter-exit times {Tk} are independent and identically distributed random variables with CDF P(Xk < X) = G(x) and Y is distributed, independently of {Xk} and {tk}, as an exponentiated exponential law with CDF H(y) = P(Y < y) = (1 − e−λy)n. The mean waiting time to break down of the system has been obtained assuming B(t) to be heavy tailed and as well as light tailed. For the exponential case of G(x), a comparative study has also been made between heavy tailed mean waiting time to break down and light tailed mean waiting time to break down values. The recruitment policy operating under the heavy tailed case is shown to be more economical in all types of man- power systems.
2014-01-01T00:00:00ZModeling severity of tuberculosis as a multiple cause of death in South AfricaForcheh, NtonghanwahSetlhare, KeamogetseAmey, Alphonse K.A.http://hdl.handle.net/10311/18872019-02-16T00:01:39Z2014-01-01T00:00:00ZModeling severity of tuberculosis as a multiple cause of death in South Africa
Forcheh, Ntonghanwah; Setlhare, Keamogetse; Amey, Alphonse K.A.
The multiple cause of death (MCOD) analysis is used to account for the full contribution of TB as acause of death to South African mortality in 2008 that were coded using ICD10. Following a review of MCOD methods, a sufficient set of variables for use in MCOD and a new method of quantifying the severity of each cause of death are proposed. The results show that a total of 86,818 (14.3% ofall deaths) were TB related, and within all deaths due to natural underlying causes, 86,373 (16.1%)were TB related. Furthermore, 42,581 (7.9%) were due to TB only, 6.0% had TB as an underlying cause along with other contributory causes and 2.0% had TB as a contributory cause. TB was mentioned as the underlying cause of death in 74,863 certificates or 13.9% of deaths due to naturalunderlying causes. Further analysis using multinomial baseline logit models, reveals that the
relative odds of death in any demographic group compared with death in the baseline categories depend on the severity level of TB considered. It is proposed that the severity measure should be adopted when studying the contribution of all main causes of death to total mortality.
2014-01-01T00:00:00ZAlgorithm-I on splitting of sizes for sampling procedure with inclusion probabilities proportional to sizeDwivedi, Vijai Kumarhttp://hdl.handle.net/10311/18372018-12-05T00:02:43Z2016-07-01T00:00:00ZAlgorithm-I on splitting of sizes for sampling procedure with inclusion probabilities proportional to size
Dwivedi, Vijai Kumar
The sampling scheme proposed by Srivastava and Singh depends upon a specific split of the sizes and most of the πij′s do not satisfy the condition of non-negativity of variance estimates as suggested by Hanurav. Using the information about the nature of non-negativity condition (фij>0) approach, Dwivedi provided split of sizes with less number of trials which gives a set of πij ′s satisfying the condition of non-negativity of variance estimates. This paper provides an algorithm for a proper split of sizes which gives a set of πij ′s such that the condition of non-negativity of variance estimates is nearly satisfied. It is also shown that on an average the relative efficiency of proposed algorithm shows the superiority over PPSWR.
Some symbols on the abstract may not appear as the same on the original copy
2016-07-01T00:00:00Z