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B K Sinha - One of the best experts on this subject based on the ideXlab platform.

  • chapter 19 eliciting information on sensitive features block total response technique and related inference
    Handbook of Statistics, 2016
    Co-Authors: Karabi Nandy, M Marcovitz, B K Sinha
    Abstract:

    Randomized Response Technique was first introduced and popularized by Warner in 1965. Since then, survey sampling theoreticians and practitioners have contributed significantly in this area of survey methodological research. The idea is to be able to elicit a “truthful” response on sensitive feature(s) from the sampled respondents (of a finite labeled population of respondents), so that eventually the population mean of the sensitive feature can be unbiasedly estimated. Toward this, a novel technique was introduced by Raghavarao and Federer (1979) and it was termed “Block Total Response” (BTR) technique. We undertake various meaningful versions/generalizations of the BTR technique, after a brief review of the literature in this direction. In the process, we also introduce empirical Bayes estimators.

  • eliciting information on sensitive features block total response technique and related inference
    2016
    Co-Authors: Karabi Nandy, M Marcovitz, B K Sinha
    Abstract:

    Abstract Randomized Response Technique was first introduced and popularized by Warner in 1965 . Since then, survey sampling theoreticians and practitioners have contributed significantly in this area of survey methodological research. The idea is to be able to elicit a “truthful” response on sensitive feature(s) from the sampled respondents (of a finite labeled population of respondents), so that eventually the population mean of the sensitive feature can be unbiasedly estimated. Toward this, a novel technique was introduced by Raghavarao and Federer (1979) and it was termed “Block Total Response” (BTR) technique. We undertake various meaningful versions/generalizations of the BTR technique, after a brief review of the literature in this direction. In the process, we also introduce empirical Bayes estimators.

Damaraju Raghavarao - One of the best experts on this subject based on the ideXlab platform.

  • Sufficient conditions for balanced incomplete block designs to be minimal fractional combinatorial treatment designs
    Biometrika, 2003
    Co-Authors: Damaraju Raghavarao, Walter T. Federer
    Abstract:

    Sufficient conditions are given for balanced incomplete block designs with block sizes three and four to be saturated minimal fractions of m items taken in mixture sizes of n e 3 and 4 for estimating the contrasts of item means and two-item specific mixing effects. Such fractions are useful for investigations involving mixtures of crops, drugs, marketing practices and other systems using mixtures of items. The balanced incomplete block design with parameters n e 6 and k e 4 is shown to be a saturated minimal fraction for estimating contrasts of item means and two-item and three-item specific mixing effects. This is a continuation of the work of Federer & Raghavarao (1987) and Federer (2001). Copyright Biometrika Trust 2003, Oxford University Press.

  • Construction of Minimal Fractional Combinatorials
    2001
    Co-Authors: Walter T. Federer, Damaraju Raghavarao
    Abstract:

    Walter T. Federer and Damaraj u Raghavarao June 2001 Construction algorithms for minimal fractional combinatorials are described for the following situations: (i) General mixing ability (GMA) effects or items means form items. (ii) Item means and hi-specific mixing ability (BSMA) effects. (iii) Item means and mixing ability effects up to the kth specific mixing ability (KSMA). (iv) Item means, BSMA effects, and tri-specific mixing ability (TSMA) effects. Numerical examples are given for three situations involving minimal fractional combinatorials for estimating item means, BSMA effects, and TSMA effects. CONSTRUCTION OF MINIMAL FRACTIONAL COMBINATORIALS

  • Characterization of BIB Designs for Constructing Minimal Fractional Combinatorial Treatment Designs
    2000
    Co-Authors: Damaraju Raghavarao, Walter T. Federer
    Abstract:

    A characterization of balanced incomplete block (BID) designs of block sizes three and four for obtaining saturated minimal fractions of m items taken n at a time for estimating the contrasts of item means and two-item, BSMA, and three item, TSMA, specific mixing effects. Such fractions are useful for investigations involving mixtures of crops, drugs, marketing practives, and other systems utilizing mixtures of items. This is a continuation of the work of Federer and Raghavarao (1987) and Federer (2000).

  • A test for detecting untruthful answering in randomized response procedures
    Journal of Statistical Planning and Inference, 1992
    Co-Authors: Damaraju V. Lakshmi, Damaraju Raghavarao
    Abstract:

    Abstract Randomized response procedures are used to elicit responses to sensitive questions. Assuming that the probability of providing an untruthful answer is the same in sensitive and non-sensitive categories and using Warner's (1965) procedure twice, Krishnamoorthy and Raghavarao (1991) developed methods of testing this probability to be zero and estimating this probability. Using the same framework provided by them, we develop an asymptotic chi-square test and give a lower bound for the power of the test.

Walter T. Federer - One of the best experts on this subject based on the ideXlab platform.

  • On the construction of orthogonal F-squares of order n from an orthogonal array (n, k, s, 2) and an OL(s, t) set
    Journal of Statistical Planning and Inference, 2011
    Co-Authors: John P. Mandeli, Walter T. Federer
    Abstract:

    Abstract Complete sets of orthogonal F-squares of order n = sp, where g is a prime or prime power and p is a positive integer have been constructed by Hedayat, Raghavarao, and Seiden (1975). Federer (1977) has constructed complete sets of orthogonal F-squares of order n = 4t, where t is a positive integer. We give a general procedure for constructing orthogonal F-squares of order n from an orthogonal array (n, k, s, 2) and an OL(s, t) set, where n is not necessarily a prime or prime power. In particular, we show how to construct sets of orthogonal F-squares of order n = 2sp, where s is a prime or prime power and p is a positive integer. These sets are shown to be near complete and approach complete sets as s and/or p become large. We have also shown how to construct orthogonal arrays by these methods. In addition, the best upper bound on the number t of orthogonal F(n, λ1), F(n, λ2), …, F(n, λ1) squares is given.

  • Sufficient conditions for balanced incomplete block designs to be minimal fractional combinatorial treatment designs
    Biometrika, 2003
    Co-Authors: Damaraju Raghavarao, Walter T. Federer
    Abstract:

    Sufficient conditions are given for balanced incomplete block designs with block sizes three and four to be saturated minimal fractions of m items taken in mixture sizes of n e 3 and 4 for estimating the contrasts of item means and two-item specific mixing effects. Such fractions are useful for investigations involving mixtures of crops, drugs, marketing practices and other systems using mixtures of items. The balanced incomplete block design with parameters n e 6 and k e 4 is shown to be a saturated minimal fraction for estimating contrasts of item means and two-item and three-item specific mixing effects. This is a continuation of the work of Federer & Raghavarao (1987) and Federer (2001). Copyright Biometrika Trust 2003, Oxford University Press.

  • Construction of Minimal Fractional Combinatorials
    2001
    Co-Authors: Walter T. Federer, Damaraju Raghavarao
    Abstract:

    Walter T. Federer and Damaraj u Raghavarao June 2001 Construction algorithms for minimal fractional combinatorials are described for the following situations: (i) General mixing ability (GMA) effects or items means form items. (ii) Item means and hi-specific mixing ability (BSMA) effects. (iii) Item means and mixing ability effects up to the kth specific mixing ability (KSMA). (iv) Item means, BSMA effects, and tri-specific mixing ability (TSMA) effects. Numerical examples are given for three situations involving minimal fractional combinatorials for estimating item means, BSMA effects, and TSMA effects. CONSTRUCTION OF MINIMAL FRACTIONAL COMBINATORIALS

  • Characterization of BIB Designs for Constructing Minimal Fractional Combinatorial Treatment Designs
    2000
    Co-Authors: Damaraju Raghavarao, Walter T. Federer
    Abstract:

    A characterization of balanced incomplete block (BID) designs of block sizes three and four for obtaining saturated minimal fractions of m items taken n at a time for estimating the contrasts of item means and two-item, BSMA, and three item, TSMA, specific mixing effects. Such fractions are useful for investigations involving mixtures of crops, drugs, marketing practives, and other systems utilizing mixtures of items. This is a continuation of the work of Federer and Raghavarao (1987) and Federer (2000).

Karabi Nandy - One of the best experts on this subject based on the ideXlab platform.

  • chapter 19 eliciting information on sensitive features block total response technique and related inference
    Handbook of Statistics, 2016
    Co-Authors: Karabi Nandy, M Marcovitz, B K Sinha
    Abstract:

    Randomized Response Technique was first introduced and popularized by Warner in 1965. Since then, survey sampling theoreticians and practitioners have contributed significantly in this area of survey methodological research. The idea is to be able to elicit a “truthful” response on sensitive feature(s) from the sampled respondents (of a finite labeled population of respondents), so that eventually the population mean of the sensitive feature can be unbiasedly estimated. Toward this, a novel technique was introduced by Raghavarao and Federer (1979) and it was termed “Block Total Response” (BTR) technique. We undertake various meaningful versions/generalizations of the BTR technique, after a brief review of the literature in this direction. In the process, we also introduce empirical Bayes estimators.

  • eliciting information on sensitive features block total response technique and related inference
    2016
    Co-Authors: Karabi Nandy, M Marcovitz, B K Sinha
    Abstract:

    Abstract Randomized Response Technique was first introduced and popularized by Warner in 1965 . Since then, survey sampling theoreticians and practitioners have contributed significantly in this area of survey methodological research. The idea is to be able to elicit a “truthful” response on sensitive feature(s) from the sampled respondents (of a finite labeled population of respondents), so that eventually the population mean of the sensitive feature can be unbiasedly estimated. Toward this, a novel technique was introduced by Raghavarao and Federer (1979) and it was termed “Block Total Response” (BTR) technique. We undertake various meaningful versions/generalizations of the BTR technique, after a brief review of the literature in this direction. In the process, we also introduce empirical Bayes estimators.

Narelle Smith - One of the best experts on this subject based on the ideXlab platform.

  • The Design of Scoring Schemes for Surveys Using the Block Total Response Method
    Communications in Statistics-theory and Methods, 2005
    Co-Authors: Narelle Smith
    Abstract:

    In this article we investigate the design of scoring schemes for surveys using the block total response method. This method was first proposed by Raghavarao and Federer (1979) to provide accurate estimates of the base rates of sensitive characteristics using balanced incomplete block designs. The scoring scheme used in Raghavarao and Federer (1979) did not guarantee anonymity of answers and so the possibility of improving on this basic scoring scheme is considered in this article.

  • The use of balanced incomplete block designs in designing randomized response surveys
    Australian & New Zealand Journal of Statistics, 2003
    Co-Authors: Narelle Smith, Deborah J. Street
    Abstract:

    This paper investigates the block total response method proposed by Raghavarao and Federer for providing accurate estimates of the base rates of sensitive characteristics during surveys. It determines the best balanced incomplete block design to use to estimate the base rates for three, four, five and six sensitive attributes respectively, given a maximum total number of 13 questions. The estimates obtained from this method have smaller variance than estimates obtained using the similar, but more popular, unmatched count technique. Copyright 2003 Australian Statistical Publishing Association Inc..