Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A new integrated likelihood for estimating population size in dependent
  dual-record system   Efficient estimation of population size from dependent dual-record system
(DRS) remains a statistical challenge in capture-recapture type experiment.
Owing to the nonidentifiability of the suitable Time-Behavioral Response
Variation model (denoted as $M_{tb}$) under DRS, few methods are developed in
Bayesian paradigm based on informative priors. Our contribution in this article
is in developing integrated likelihood function from model $M_{tb}$ based on a
novel approach developed by Severini (2007, Biometrika). Suitable weight
function on nuisance parameter is derived under the assumption of availability
of knowledge on the direction of behavioral dependency. Such pseudo-likelihood
function is constructed so that the resulting estimator possess some desirable
properties including invariance and negligible prior (or weight) sensitiveness.
Extensive simulations explore the better performance of our proposed method in
most of the situations than the existing Bayesian methods. Moreover, being a
non-Bayesian estimator, it simply avoids heavy computational effort and time.
Finally, illustration based on two real life data sets on epidemiology and
economic census are presented.

25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
1
The categories of this paper are: cs