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Research article Special Issues

Impact of estrogen population pharmacokinetics on a QSP model of mammary stem cell differentiation into myoepithelial cells

  • Received: 17 January 2021 Accepted: 16 July 2021 Published: 28 July 2021
  • MSC : 92B05, 92C32

  • Stem cell differentiation cascades are critical components of healthy tissue maintenance. Dysregulation in these systems can lead to serious diseases, including cancer. Myoepithelial mammary cells are produced from differentiated mammary stem cells in processes regulated, in part, by estrogen signalling and concentrations. To quantify and predict the production of mammary myoepithelial cell production by estrogen, we developed a mechanistic, quantitative systems pharmacology (QSP) model that includes the explicit characterization of free and unbound estrogen concentrations in circulation. Linking this model to a previously developed population pharmacokinetics model for ethinyl estradiol, a synthetic form of estrogen included in oral contraceptives, we predicted the effects of estrogen on myoepithelial cell development. Interestingly, pharmacokinetic intraindividual variability alone did not significantly impact on our modelos predictions, suggesting that combinations of physiological and pharmacokinetic variability drive heterogeneity in mechanistic QSP models. Our model is one component of an improved understanding of mammary myoepithelial cell production and development, and our results support the call for mechanistically constructed systems models for disease and pharmaceutical modelling.

    Citation: Justin Le Sauteur-Robitaille, Zhe Si Yu, Morgan Craig. Impact of estrogen population pharmacokinetics on a QSP model of mammary stem cell differentiation into myoepithelial cells[J]. AIMS Mathematics, 2021, 6(10): 10861-10880. doi: 10.3934/math.2021631

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  • Stem cell differentiation cascades are critical components of healthy tissue maintenance. Dysregulation in these systems can lead to serious diseases, including cancer. Myoepithelial mammary cells are produced from differentiated mammary stem cells in processes regulated, in part, by estrogen signalling and concentrations. To quantify and predict the production of mammary myoepithelial cell production by estrogen, we developed a mechanistic, quantitative systems pharmacology (QSP) model that includes the explicit characterization of free and unbound estrogen concentrations in circulation. Linking this model to a previously developed population pharmacokinetics model for ethinyl estradiol, a synthetic form of estrogen included in oral contraceptives, we predicted the effects of estrogen on myoepithelial cell development. Interestingly, pharmacokinetic intraindividual variability alone did not significantly impact on our modelos predictions, suggesting that combinations of physiological and pharmacokinetic variability drive heterogeneity in mechanistic QSP models. Our model is one component of an improved understanding of mammary myoepithelial cell production and development, and our results support the call for mechanistically constructed systems models for disease and pharmaceutical modelling.



    In large scale surveys, it is a usual practice to prefer multi-stage sampling to estimate the population characteristics over single-stage sampling. The main purpose to use multi-stage sampling is the clear reduction in the cost of survey operations even if estimates derived from multi-stage sampling are likely to be less efficient than those of the single-stage sampling. Sukhatme et al. [1] advised some ratio and regression type estimators in two-stage sampling using single auxiliary variable when first stage units are of unequal or equal sizes. The suitable use of auxiliary information in estimation stage results as a considerable reduction in the mean square error of the estimator. By making use of auxiliary information, Srivastava and Garg [2] proposed separate-type estimator for the estimation of population mean in two-stage sampling design. Taking inspiration from Srivastava and Garg [2], Koyuncu and Kadilar [3] and Jabeen et al. [4] proposed separate type estimator under two-stage sampling. In the literature, the use of two-phase sampling under two-stage sampling design is not well documented. Saini and Bahl [5] proposed estimator under two-stage sampling design using double sampling for stratification and multi-auxiliary information. A generalized ratio cum product estimator for population mean in simple random sampling was developed by Singh et al. [6]. Shabbir [7] produces the estimators of population mean under stratified two phase sampling.

    In the literature ([8,9,10,11]), the use of two phase sampling under two stage sampling design is not well documented. Also the shrinkage estimators have been discussed several times in literature by considering the unbiasedness of the estimators but no one has discussed the situation when the property of unbiasedness is not fulfill that is very common in real life applications. In order to fill this gap, we are motivated to produce two-exponential estimators under two stage two phase sampling that is discussed in section 2. Also we will discuss general shrinkage estimator in section 3. We will compare both estimators mathematically and by using real population data in section 4 and 5. Finally the conclusion will be discussed in section 6.

    Let a population consists of N first stage units, each containing Mi second-stage units where i = 1, 2, …, N. Let a first-stage sample of size n (N) is selected and subsequently a second-stage sample of mi(1)(Mi) units is selected and information on some auxiliary variables say xij(1) is taken. Here it is assumed that each first stage unit/cluster is of different size so each cluster is assigned a weight ηi=MiˉM to it. Let a sub-sample (second-phase sample) of mi(2) units is selected from mi(1)(first-phase sample) such that mi(2)Mi. Let mi(2) units are observed so as to collect information regarding study variable yij(2) and auxiliary variablesxij(2). Let ˉXts=1NNi=11ˉMMiˉXi. be the mean in the population and ˉXi=1MiMij=1xij be the mean of ith first stage unit in the population. Let ˉxts(1)=1nni=1ηiˉxi(1) and ˉxi(2)=1mi(2)mi(2)j=1xij(2) respectively are the means of first-phase and second-phase sample in two-stage sampling where ˉxi(1) and ˉxi(2) be the sample means of first phase and second phase in ith stage. Let S2xb=1N1Ni=1(ηiˉXiˉXts)2 be the population variance between fsu’s and S2xwi=1Mi1ni=1(xijˉXi.)2 be the population variance within first stage units. Similarly these notations can be defined for other variables. Further we consider that the selection of units at each stage (or phase) has been made by simple random sampling without replacement.

    We propose a generalized estimator by considering the exponential relationship as:

    tG=ˉytsexp(α(1aˉxts(1)(ˉX+(a1)ˉxts(1))))exp(β(1bˉzts(2)(ˉZ+(b1)ˉzts(2)))), (1)

    Where (a, b) are constants to be determined such that the mean square error is minimum, (α, β) are known constants takes the value (0, 1, -1) to produce different ratio-type and product-type estimators as presented in Table B1 (Appendix B).

    To derive the bias and mean square error, we proceed as follows:

    ˉyts(2)ˉYtsˉYts=e0(2),ˉxts(1)ˉXtsˉXts=e1(1),ˉzts(2)ˉZtsˉZts=e2(2),ˉxts(2)ˉXtsˉXts=e1(2),ˉzts(1)ˉZtsˉZts=e2(1)

    Further we assume that E(e0(2))=E(e1(2))=E(e2(2))=E(e2(1))=E(e1(1))=0, and some expectations under two-stage sampling design are obtained in order to obtain the bias and mean square error as,

    E(e0(2)2)=C020(2),E(e1(2)2)=C200(2),E(e2(2)2)=C002(2),E(e1(1)2)=C200(1),E(e2(1)2)=C002(1),E(e1(2)e1(1))=C200(1)E(e0(2)e1(2))=C110(2),E(e1(2)e2(2))=C101(2),E(e0(2)e2(2))=C011(2),E(e0(2)e1(1))=C110(1),E(e1(2)e2(1))=C101(1),E(e0(2)e2(1))=C011(1)WhereC020(2)=1ˉY2ts{(1n1N)S2yb+1nNNi=1η2i(1mi(2)1Mi)S2ywi,},C200(1)=1ˉX2ts{(1n1N)S2xb+1nNNi=1η2i(1mi(1)1Mi)S2xwi}C200(2)=1ˉX2ts{(1n1N)S2xb+1nNNi=1η2i(1mi(2)1Mi)S2xwi},C002(1)=1ˉZ2ts{(1n1N)S2zb+1nNNi=1η2i(1mi(1)1Mi)S2zwi},C002(2)=1ˉZ2ts{(1n1N)S2zb+1nNNi=1η2i(1mi(2)1Mi)S2zwi},C110(1)=1ˉYtsˉXts{(1n1N)ρxybSybSxb+1nNNi=1η2i(1mi(1)1Mi)ρxywiSywiSxwi},C110(2)=1ˉYsˉXs{(1n1N)ρxybSybSxb+1nNNi=1η2i(1mi(2)1Mi)ρxywiSywiSxwi},C101(1)=1ˉZtsˉXts{(1n1N)ρxzbSzbSxb+1nNNi=1η2i(1mi(1)1Mi)ρxzwiSzwiSxwi},C101(2)=1ˉZtsˉXts{(1n1N)ρxzbSzbSxb+1nNNi=1η2i(1mi(2)1Mi)ρxzwiSzwiSxwi},C200(1)=1ˉZtsˉXts{(1n1N)SxbSxb+1nNNi=1η2i(1mi(1)1Mi)SxwiSxwi}C011(2)=1ˉZtsˉYts{(1n1N)ρyzbSzbSyb+1nNNi=1η2i(1mi(2)1Mi)ρyzwiSzwiSywi},C011(1)=1ˉZtsˉYts{(1n1N)ρyzbSzbSyb+1nNNi=1η2i(1mi(1)1Mi)ρyzwiSzwiSywi}} (2)

    Using (2) we can express (1) to derive the bias and mean square error as,

    tG=ˉYts(1+e0(2))exp[αae1(1)(1+(a1)ae1(1))1]exp[βbe2(2)(1+(b1)be2(2))1], (3)

    If |e1(1)|<1, we expand the series, (1+(a1)ae1(1))1 and (1+(b1)be2(2))1 up to the order n-1, we get,

    tG=ˉYts(1+e0(2))exp[αae1(1)(1(a1)ae1(1)+(a1)2a2e1(1)2+...)]exp[βbe2(2)(1(b1)be2(2)+(b1)2b2e2(2)2+...)], (4)

    If the contribution of terms involving powers in e0(2), e1(1) and e2(2) higher than two is negligible, we have;

    tG=ˉYts(1+e0(2))[1αae1(1)+α(a1)2a2e21(1)+12α2a2e21(1)][1βbe2(2)+β(b1)2b2e22(2)+12β2b2e22(2)], (5)

    or

    tGˉYts=ˉYts[e0(2)αae1(1)+α(a1)a2e21(1)+αβabe1(1)e2(2)+α22a2e21(1)βbe2(2)+β22b2e2(2)2+β(b1)b2e2(2)2βbe2(2)e0(2)αae1(1)e0(2)], (6)

    In order to get the bias, we take expectation on (6) and get,

    Bias(tG)=ˉYts[α22a2C200(1)+α(a1)a2C200(1)+β22b2C002(2)+β(b1)b2C002(2)βbC011(2)αaC110(1)+αaβbC101(1)], (7)

    To get the mean square error of the estimator, we take square and retain terms up to first order of e’s then we take expectation of (6) and we obtain,

    MSE(tG)=ˉY2ts(C020(2)+(αa)2C200(1)+(βb)2C002(2)2αaC110(1)2βbC011(2)+2αaβbC101(1)), (8)

    For the following optimal value of the constants a and b, we achieve the minimum Variance among the class of proposed generalized estimator,

    a=α(C200(1)C002(2)C101(2)2)C110(1)C002(2)C011(1)C101(2),andb=β(C200(1)C002(2)C101(2)2)C200(1)C011(1)C110(1)C101(2), (9)

    We obtain minimum mean square error as,

    minMSE(tG)=ˉY2ts(C020(2)C110(1)2C002(2)+C011(1)2C200(1)2C110(1)C101(2)C011(1)C200(1)C002(2)C101(2)2), (10)

    We observe from (10) that proposed generalized estimator gives us more precise results under the optimal conditions, as compare to its class of the estimators.

    From (7)–(10), we get expressions of the bias, mean square error, optimal values and minimum mean square error for the exponential-type estimators presented in Table B1 as class of estimators. Some of the results are discussed as,

    ⅰ). For α=1,β=1, we get a class of exponential-type ratio-cum-ratio estimators given in Table B1 and expressions of the mean square error and bias for these estimators are given as,

    MSE(tjerr)={ˉY2ts(C020(2)+1a2C200(1)+1b2C002(2)21aC110(1)21bC011(2)+21abC101(1))j(G)=1,3,5},andBias(tjerr)={ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)1aC110(1))j(G)=1,3,5},} (11)

    Substitution of the different values of a and b yield the mean square error and bias for the estimators belongs to the class of exponential-type ratio-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential ratio-cum-ratio estimators is obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C200(1)C002(2)C101(2)2C200(1)C011(1)C110(1)C101(2).

    ⅱ). For α=1,β=1, we get exponential-type product-cum-product estimators given in Table B1.The mean square error of tG are expressed using as,

    MSE(tjepp)={ˉY2ts(C020(2)+1a2C200(1)+1b2C002(2)+21aC110(1)+21bC011(2)+21abC101(1)),j(G)=2,4,6}andBiastjepp)={ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)+1aC110(1)),j(G)=2,4,6}) (12)

    Substitution of the different values of “a” andb” yield the Mean Square Error and bias for the estimators belongs to the class of exponential-type ratio-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential product-cum-product estimators is obtained as,

    a=C101(2)2C200(1)C002(2)C110(1)C002(2)C011(1)C101(2)andb=C101(2)2C200(1)C002(2)C200(1)C011(1)C110(1)C101(2).

    ⅲ). For α=1,β=1, we get exponential-type product-cum-ratio estimators given in Table B1.The mean square error of tG is expressed as,

    MSE(tjerp)={ˉY2ts(C020(2)+1aC200(1)+1bC002(2)+21aC110(1)21bC011(2)21abC101(1)),j(G)=7,9,11}andBias(tjerp)={ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)+1aC110(1)),j(G)=7,9,11}) (13)

    Substitution of the different values of “a” and “b” yield the mean square error and bias for the estimators belongs to the class of exponential-type ratio-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential product-cum-ratio estimators are obtained as,

    a=C101(2)2C200(1)C002(2)C110(1)C002(2)C011(1)C101(2)andb=C200(1)C011(1)C110(1)C101(2)C200(1)C002(2)C101(2)2.

    ⅳ). For α=1,β=1, we get exponential-type ratio-cum-product estimators given in Table A1. The mean square error of tG is expressed as,

    MSE(tjepr)={ˉY2ts(C020(2)+1aC200(1)+1bC002(2)21aC110(1)+21bC011(2)21a1bC101(1)),j(G)=8,10,12}andBias(tjepr)={ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)+1bC101(2)1aC110(1)),j(G)=8,10,12}) (14)

    Substitution of the different values of “a” and “b” yield the mean square error and bias for the estimators belongs to the class of exponential-type ratio-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential ratio-cum-product estimators, are obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C1012C200(1)C002(2)C200(1)C011(1)C110(1)C101(2).

    It is to mention that minimum mean square error of tjepr, tjepr, tjepr, and tjepr in (11)–(14) will be same but the conditions under which these attain minimum value are not unique.

    In literature, the shrinkage estimators have been discussed several times considering the property that the estimators are unbiased. In many practical situations the property of unbiasedness is not met so consequently the existing literature will not be helpful. Such deviations from the said property have motivated us to consider the shrinkage estimators following the property of biasedness. Important results are presented in theorem and subsequently proof is provided in Appendix A.

    For any general estimator t, let ts be a general shrinkage estimator for estimating any population characteristic T (e.g., population mean) which is unknown to us,

    ts=λt (15)

    A general form for the bias and mean square error of a general shrinkage estimator up to the first order of approximation may be given as,

    Bias(ts)=(λ1)T+Bias(t) (16)

    and

    MSE(ts)=T2(λ1)2+λ2MSE(t)+2Tλ(λ1)Bias(t) (17)

    And form of the optimal shrinkage estimator in (15) along with minimum bias and minimum mean square error will be as,

    ts=(T2+MSE(t)+2TBias(t))T(T+Bias(t)t (18)
    Bias(topts)=((T2+MSE(t)+2TBias(t))T(T+Bias(t)1)T+Bias(t) (19)

    and

    minMSE(ts)=T2T2(T+Bias(t)T2+MSE(t)+2TBias(t) (20)

    Proof: For details see Appendix A.

    By using (15), generalized form of a shrinkage class of estimator is,

    tGs=λtG

    where

    tG=ˉytsexp(α(1aˉxts(1)(ˉXts+(a1)ˉxts(1))))exp(β(1bˉzts(2)(ˉZts+(b1)ˉzts(2)))) (21)

    For different choices of the values of the constants α,β,a,b, we get different family of shrinkage estimators as given in Table B2 (See Appendix).

    Following the Theorem, we can directly write the mean square error and bias expressions of tGs using (7) and (8) respectively as,

    Bias(tGs)=λˉYts[α(a1)a2C200(1)+α22a2C200(1)+β(b1)b2C002(2)+β22b2C002(2)βbC101(2)αaC110(1)]+ˉYts(λ1),or
    Bias(tGs)=λBias(tGe)+ˉYts(λ1), (22)

    and

    MSE(tGs)=λ2ˉY2ts(C020(2)+(αa)2C200(1)+(βb)2C002(2)2αaC110(1)2βbC011(2)+2αaβbC101(1))+(λ1)2ˉYts2+2λ(λ1)ˉYts[α(a1)a2C200(1)+α22a2C200(1)+β(b1)b2C002(2)+β22b2C002(2)βbC101(2)αaC110(1)]

    or

    MSE(tGs)=λ2MSE(tGe)+(λ1)2ˉYts2+2ˉYtsλ(λ1)MSE(tGe) (23)

    The optimal value of λ, which minimize the expression (23) is obtained as,

    λ=(T2+MSE(tGe)+2TBias(tGe))T(T+Bias(tGe) (24)

    Using (23), the optimal estimator for the shrinkage family of estimator (tGs) is written as

    tGs=ˉyts(ˉYts2+MSE(tGe)+2ˉYtsBias(tGe))ˉYts(ˉYts+Bias(tGe)exp(α(1aˉxts(1)(ˉXts+(a1)ˉxts(1))))exp(β(1bˉzts(2)(ˉZts+(b1)ˉzts(2))))} (25)

    The expression for minimum value of min MSE(tGs) is obtained as,

    minMSE(tGs)=ˉY2tsˉY2ts(ˉYts+Bias(tGe))2[ˉY2ts+MSE(tGe)+2ˉYtsBias(tGe)]}minMSE(tGs)=ˉY2ts[1(1+Bias(ta)ˉYts)2[1+MSE(ta)ˉY2ts+2Bias(ta)ˉYts]] (26)

    From (22)–(26), we get expressions of the bias, mean square error, optimal values and minimum mean square error for the exponential-type estimators presented in Table B2 (See Appendix B) as class of estimators. Some of the results are discussed as,

    ⅰ). For α=1,β=1, we get a class of exponential-type ratio-cum-ratio estimators given in Table B2 (See Appendix) and expressions of the mean square error and bias for these estimators are given as,

    MSE(tjerrs)={ˉY2ts[1(1+Bias(tierr)ˉYts)2[1+MSE(tierr)ˉY2ts+2Bias(tierr)ˉY2ts]]j(G)=1,3,5}andBias(tjerrs)={ˉYts(λ(Bias(tjerr)ˉYts)+(λ1))j(G)=1,3,5} (27)

    Substitution of the different values of a and b produce the mean square error and bias for the estimators belongs to the class of exponential-type ratio-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential product-cum-ratio estimators are obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C200(1)C002(2)C101(2)2C200(1)C011(1)C110(1)C101(2),λ=(T2+MSE(tGe)+2TBias(tGe))T(T+Bias(tGe),whereMSE(tG)=ˉYts2[C020(2)+1a2C200(1)+1b2C002(2)21aC110(1)21bC011(2)+21a1bC101(1)].Bias(tG)=ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)1aC110(1))

    ⅱ). For α=1,β=1, we get exponential-type product-cum-product estimators given in Table B2 (See Appendix B).The mean square error of tGs is expressed using as,

    MSE(tjepps)={ˉY2ts[1(1+Bias(tierr)ˉYts)2[1+MSE(tierr)ˉY2ts+2Bias(tierr)ˉY2ts]]j(G)=2,4,6}andBias(tjepps)={ˉYts(λBias(tjepps)ˉYts+(λ1))j(G)=2,4,6} (28)

    Substitution of the different values of a and b yield the mean square error and bias for the estimators belongs to the class of exponential-type product-cum-product estimators. The optimal values which lead to minimum mean square error for the class of exponential product-cum-ratio estimators are obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C200(1)C002(2)C101(2)2C200(1)C011(1)C110(1)C101(2),λ=(T2+MSE(tGe)+2TBias(tGe))T(T+Bias(tGe),whereMSE(tG)=ˉYts2[C020(2)+1a2C200(1)+1b2C002(2)21aC110(1)21bC011(2)+21a1bC101(1)].BiastG)=ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)1aC110(1))

    ⅲ). For α=1,β=1, we get exponential-type product-cum-ratio estimators given in Table B2. The mean square error of tGs is expressed as,

    MSE(tjerps)={ˉY2ts[1(1+Bias(tierr)ˉYts)2[1+MSE(tierr)ˉY2ts+2Bias(tierr)ˉY2ts]]j(G)=7,9,11}andBias(tjerps)={ˉYts(λBias(tjerps)ˉYts+(λ1))j(G)=7,9,11} (29)

    Substitution of the different values of a and b produce the mean square error and bias for the estimators belongs to the class of exponential-type ratio-cum-product estimators. The optimal values which lead to minimum mean square error for the class of exponential ratio-cum-product estimators, are obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C200(1)C002(2)C101(2)2C200(1)C011(1)C110(1)C101(2),λ=(T2+MSE(tGe)+2TBias(tGe))T(T+Bias(tGe),whereMSE(tGe)=ˉYts2[C020(2)+1a2C200(1)+1b2C002(2)21aC110(1)21bC011(2)+21a1bC101(1)].BiastGe)=ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)1aC110(1))

    ⅳ). For α=1,β=1, we get exponential-type ratio-cum-product estimators given in Table B2. The mean square error of tGs is expressed as,

    MSE(tjeprs)={ˉY2ts[1(1+Bias(tierr)ˉYts)2[1+MSE(tierr)ˉY2ts+2Bias(tierr)ˉY2ts]]j(G)=8,10,12}andBias(tjeprs)={ˉYts(λBias(tjeprs)ˉYts+(λ1))j(G)=8,10,12} (30)

    Substitution of the different values of a and b yield the mean square error and bias for the estimators belongs to the class of exponential-type product-cum-ratio estimators. The optimal values which lead to minimum mean square error for the class of exponential product-cum-ratio estimators, are obtained as,

    a=C200(1)C002(2)C101(2)2C110(1)C002(2)C011(1)C101(2)andb=C200(1)C002(2)C101(2)2C200(1)C011(1)C110(1)C101(2),λ=(T2+MSE(tGe)+2TBias(tGe))T(T+Bias(tGe),whereMSE(tG)=ˉYts2[C020(2)+1a2C200(1)+1b2C002(2)21aC110(1)21bC011(2)+21a1bC101(1)].Bias(tG)=ˉYts(a1a2C200(1)+12a2C200(1)+b1b2C002(2)+12b2C002(2)1bC101(2)1aC110(1))

    It is to mention that minimum mean square error of tjeprs, tjeprs, tjeprs, and tjeprs in (27)–(30) will be same but the conditions under which these attain minimum value are not unique.

    In this section, some efficiency conditions have been derived in terms of mean square error and bias. The efficiency comparison of proposed two-exponential general shrinkage estimator (see section 3) and two-exponential estimator (see section 2) with unbiased estimator in two-stage sampling. In order to derive the efficiency conditions, consider the following notations,

    A1=C020(2)+(αa)2C200(1)+(βb)2C002(2)2(αa)C110(1)2(βb)C011(2)+2(αa)(βb)C101(1)
    A2=[α(a1)a2C200(1)+α22a2C200(1)+β(b1)b2C002(2)+β22b2C002(2)βbC101(2)αaC110(1)]
    A3=C020(2)+C200(1)+C002(2)2C110(1)2C011(2)+2C101(1)
    A4=(C020(2)C110(1)2C002(2)+C011(1)2C200(1)2C110(1)C101(2)C011(1)C200(1)C002(2)C101(2)2)
    A5=[λ2+2λ(λ1)(1αC200(1)+12C200(1))]
    A6=[λ2(C110(1)βbC101(!))+λ(λ1)(C200(1)C110(1))]
    A7=[λ2((βb)2C002(2)2βbC011(2))+(λ1)2+2λ(λ1)(β(b1)b2C002(2)+(βb)2C002(2)βbC101(1))]

    The efficiency conditions may be written as:

    ⅰ. MSE(tGs)MSE(ˉyts)0 if

    min((1A2)±((1A2)2(1+A1+2A2)(C020(2)))(1A2))λmax((1A2)±((1A2)2(1+A1+2A2)(C020(2)))(1A2)), (31)

    ⅱ. MSE(tGs)MSE(tr)0

    min((1A2)±((1A2)2(1+A1+2A2)(A3))(1A2))λmax((1A2)±((1A2)2(1+A1+2A2)(A3))(1A2)) (32)

    ⅲ. MSE(tGs)MSE(tG)0

    min((1A2)±((1A2)2(1+A1+2A2)(A1))(1A2))λmax((1A2)±((1A2)2(1+A1+2A2)(A1))(1A2)) (33)

    ⅳ. MSE(tGs)minMSE(tG)0

    min((1A2)±((1A2)2(1+A1+2A2)(A4))(1A2))λmax((1A2)±((1A2)2(1+A1+2A2)(A4))(1A2)) (34)

    ⅰ. MSE(tGs)MSE(ˉyts)0 if

    min(A6±A62A5(A7C020(2))αA5)1amax(A6±A62A5(A7C020(2))αA5) (35)

    ⅱ. MSE(tGs)MSE(tr)0

    min(A6±A62A5(A7A3)αA5)1amax(A6±A62A5(A7A3)αA5) (36)

    Substituting different values of (α,β,a,b) in above equations, we get different mathematical comparisons for the estimators given in Table B2 (See Appendix B). Also similar comparison is obtained if we assume b as unknown and λ,a is known.

    For the demonstration of the performance of proposed two-exponential general shrinkage estimator, we take real population consists of four clusters with unequal first stage units. The description about the populations is given in Table B1 in Appendix B. The mean square error and percentage relative efficiency values for each of the estimators are given in Table C2.For this population, correlations within the clusters are positive (see Table C1). It is therefore the population is applicable only for ratio/ratio-type estimators.

    The performance of the proposed two-exponential general shrinkage estimator and two-exponential estimator under two stage two phase sampling has been expressed in form of their mean square error and percentage relative efficiency values in Table C2. From Table C2 we see that the minimum mean square error of two-exponential general shrinkage estimator tGs that is equal to the mean square error of t5s. The percentage relative efficiency demonstrate the same result as the percentage relative efficiency of the proposed two-exponential general shrinkage is high among the family of proposed two-exponential general shrinkage and t5s is the same efficient as tGs.

    We also see that the two-exponential general shrinkage estimator (section 3) is better to be used as compare to two-exponential estimator (section 2). The performance of t1s is better than t1, similarly t3s performs better than t3. So it is concluded that for this particular population the use of two-exponential general shrinkage estimator in two stage two phase sampling produce better results.

    Finally from the above empirical results, it is concluded that the performance of the two-exponential general shrinkage estimator (section 3) is higher as compare to proposed two-exponential estimator in two stage two phase sampling. So the two-exponential general shrinkage estimator (section 3) is acceptable for the real life application in two stage two phase sampling design.

    We are grateful to the anonymous reviewers for their valuable comments and suggestions in improving the manuscript.

    The authors declare no conflict of interest.

    Proof of Theorem. Let t be any general estimator of some population parameter T in two stage sampling for which a general shrinkage estimator ts is defined as,

    ts=λt, (37)

    By definition the bias of ts is

    Bias(ts)=λE(t)T

    or

    =λE(tT)+(λ1)T

    or

    =λBias(t)+(λ1)T (38)

    By definition the MSE of ts may be defined as,

    MSE(ts)=E(tsT)2

    or

    =E(λtT)2

    consider t=T(1+eT), with E(eT)=0 and E(eT2)=Var(t)T2=MSE(t)T2.

    =E{λ(T(1+eT))T}2

    or

    =T2E{λ2(1+eT)2+12λ(1+eT)}

    or

    MSE(ts)=T2{λ2(1+MSE(t)T2)+12λ} (39)

    We may also write (B-3) as

    MSE(ts)=λ2MSE(t)+(λ1)2T2

    or

    MSE(ts)=λ2T2C2t+(λ1)2T2WhereC2t=MSE(t)T2 (40)

    Where Ct is a coefficient of variation of an estimator t.

    Partially differentiating (40) w.r.t λ and simplifying, we get:

    λ=11+T2MSE(t), (41)

    or

    λ=11+C2t, (42)

    Now using (41) in (15), (16) and (17) respectively, we may get,

    ts=t1+T2MSE(t) (43)
    Bias(ts)=11+T2MSE(t)[Bias(t)+T2MSE(t)T] (44)
    MSEmin(ts)=MSE(t)1+T2MSE(t) (45)

    Alternatively we may also produce the expressions in (44) to (46) as,

    ts=t1+C2t (46)
    Bias(ts)=11+C2t(Bias(t)+C2tT) (47)
    MSEmin(ts)=T2C2t1+C2t (48)
    Table B1.  Some members of the proposed two exponential estimator (tG).
    Ratio-cum-Ratio estimator
    α=1,β=1
    Product -cum-product estimator
    α=1,β=1
    a b
    t1err=ˉyts(2)exp(ˉXtsˉxts(1)ˉXts+ˉxts(1))exp(ˉZtsˉzts(2)ˉZts+ˉzts(2)) t2epp=ˉyts(2)exp(ˉxts(1)ˉXtsˉxts(1)+ˉXts)exp(ˉzts(2)ˉZtsˉzts(2)+ˉZts) 2 2
    t3err=ˉyts(2)exp(ˉXtsˉxts(1)ˉXts)exp(ˉZtsˉzts(2)ˉZts) t4epp=ˉyts(2)exp(ˉxts(1)ˉXtsˉXts)exp(ˉzts(2)ˉZtsˉZts) 1 1
    t5err=ˉyts(2)exp(ˉXtsˉxts(1)ˉXts+(a1)ˉxts(1))exp(ˉZtsˉzts(2)ˉZts+(b1)ˉzts(2)) t6epp=ˉyts(2)exp(ˉxts(1)ˉXtsˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) a b
    Product -cum-ratio estimator
    α=1,β=1
    Ratio-cum-product estimator
    α=1,β=1
    a b
    t7epr=ˉytsexp(ˉXtsˉxts(1)ˉXts+ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+ˉzts(2)) t8erp=ˉyts(2)exp(ˉxts(1)ˉXtsˉxts(1)+ˉXts)exp(ˉzts(2)ˉZtsˉzts(2)+ˉZts) 2 2
    t9epr=ˉyts(2)exp(ˉXtsˉxts(1)ˉXts)exp(ˉzts(1)ˉZtsˉZts) t10erp=ˉyts(2)exp(ˉxts(1)ˉXtsˉXts)exp(ˉzts(2)ˉZtsˉZts) 1 1
    t11epr=ˉyts(2)exp(ˉXtsˉxts(1)ˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) t12erp=ˉyts(2)exp(ˉxts(1)ˉXtsˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) a b

     | Show Table
    DownLoad: CSV
    Table B2.  Some members of the generalized shrinkage estimator (tGs).
    Ratio-cum-product estimator
    α=1,β=1
    Product -cum-product estimator
    α=1,β=1
    a λ b
    t1errs=λˉytsexp(ˉXtsˉxts(1)ˉXts+ˉxts(1))exp(ˉZtsˉzts(2)ˉZts+ˉzts(2)) t2epps=λˉytsexp(ˉxts(1)ˉXtsˉXts+ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+ˉzts(2)) 2 λ 2
    t3errs=λˉytsexp(ˉXtsˉxts(1)ˉXts)exp(ˉZtsˉzts(2)ˉZts) t4epps=λˉytsexp(ˉxts(1)ˉXtsˉXts)exp(ˉzts(2)ˉZtsˉZts) 1 λ 1
    t5erps=λˉytsexp(ˉXtsˉxts(1)ˉXts+(a1)ˉxts)exp(ˉZtsˉzts(2)ˉZts+(b1)ˉzts(2)) t6epps=λˉytsexp(ˉxts(1)ˉXtsˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) a λ b
    Product -cum-ratio estimator
    α=1,β=1
    Ratio-cum-product estimator
    α=1,β=1
    a λ b
    t7eprs=λˉytsexp(ˉXtsˉxts(1)ˉXts+ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+ˉzts(2)) t8erps=λˉyts(2)exp(ˉxts(1)ˉXtsˉxts(1)+ˉXts)exp(ˉzts(2)ˉZtsˉzts(2)+ˉZts) 2 λ 2
    t9eprs=λˉyts(2)exp(ˉXtsˉxts(1)ˉXts)exp(ˉzts(1)ˉZtsˉZts) t10erps=λˉyts(2)exp(ˉxts(1)ˉXtsˉXts)exp(ˉzts(2)ˉZtsˉZts) 1 λ 1
    t11eprs=λˉyts(2)exp(ˉXtsˉxts(1)ˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) t12erps=λˉyts(2)exp(ˉxts(1)ˉXtsˉXts+(a1)ˉxts(1))exp(ˉzts(2)ˉZtsˉZts+(b1)ˉzts(2)) a λ b

     | Show Table
    DownLoad: CSV
    Table C1.  Data Statistics for Population-Ⅰ.
    Population-Ⅰ (unequal fsu’s)
    Cluster 1 2 3 4
    Mi 18 14 12 20
    mi 9 7 6 10
    ˉYi 25.77722 22.79286 28.43500 23.0905
    ˉXi 51.06389 46.49700 67.00217 57.11855
    Cyi2 0.58025 0.39297 0.34783 0.31545
    Cxi2 0.43322 0.29984 0.41947 0.40689
    ρi1 0.88373 0.83895 0.82425 0.82113

     | Show Table
    DownLoad: CSV
    Table C2.  MSEs and PREs of the adapted and suggested class of estimators.
    Estimators MSE PRE With new fraction
    ¯ys 24.44649 100
    t1err 11.66857 209.51
    t3err 20.27969 120.5467
    t5err 11.46787 213.1738
    t1errs 11.58499 211.0229
    t3errs 18.92219 129.1974
    t5errs 11.25906 217.1317
    tGopts 11.25906 217.1317

     | Show Table
    DownLoad: CSV


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