Antonio Arcos

Stability of lactobacilli encapsulated in various microbial polymers.

Various microbial polymers, namely xanthan gum, gellan gum, pullulan gum and jamilan, were tested as a suitable encapsulating material for Lactobacillus plantarum CRL 1815 and Lactobacillus rhamnosus ATCC 53103. Resulting capsules were also studied for their pH and simulated gastrointestinal conditions tolerance. The morphology of the microcapsules was studied using scanning electron microscopy. pH tolerance was tested at pH 2.0, 3.5, 5.0 and 6.5 over a 6h incubation period. Simulated gastrointestinal conditions were assayed with simulated gastric and pancreatic juices and simulated bile over a 24h incubation period. Suspensions of probiotic organisms were used as a control. The results from encapsulation with microbial polymers indicate that mixtures of 1% xanthan gum with 0.75% gellan gum and 1% jamilan with 1% gellan gum were the most suitable for microencapsulation. Results for the pH tolerance tests showed no improvement in the viability of cells in relation to the control, except for pH 2.0 where lactobacilli encapsulated in xanthan:gellan gum (1%:0.75%) prolonged their viability by 6h exposure. Xanthan:gellan gum (1%:0.75%) was the most effective of the encapsulating materials tested in protecting L. plantarum and L. rhamnosus against simulated bile, improving its viability in 1-2 logCFU when compared with control. The results of this study suggest that microbial polymers are an interesting source of encapsulating material that should be taken into account for prospective studies of probiotic encapsulation for oral delivery applications.

Difference estimators of quantiles in finite populations

This paper deals with the inference of finite populations quantiles by using auxiliary information. The population information considered on the proposed estimatiors is a population quantile of the auxiliary variable with the same order as that of the quantile of the main variable to be estimated. A simulation study based on three real finite populations is performed and comparisons of the proposed estimators with other common estimators for quantile estimation are carried out.

Mean estimation with calibration techniques in presence of missing data

The problem of estimating the population mean using calibration estimators when some observations on the study and auxiliary characteristics are missing from the sample, is considered. Some new classes of estimators are proposed for any sampling design. These new classes employ to all observation (incomplete cases too) in the estimation without using any imputation techniques. On the basis of properties derived and some simulation results, the proposed estimators are compared with other complete case estimators.

Indirect methods of imputation of missing data based on available units

One of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data. Many statistical procedures cannot be used immediately if any values are missing. Imputation of missing data before starting statistical analysis is then necessary. This paper proposes imputation methods of the mean based on indirect estimators of available cases. A complete simulation study was performed to test the proposed techniques.

The use of Quantiles of Auxiliary Variables to Estimate Medians

Summary This paper proposes the use of multi-auxiliary information using quantiles and ratio and difference type estimators of the finite population distribution function to derive confidence intervals for medians. A simulation study based on three real populations compares its behaviour to that of standard methods.

Some improved estimators of finite population quantile using auxiliary information in sample surveys

Abstract The problem of quantile estimation using quantiles Qx(α) in which the order of the auxiliary variable is different from that of the main variable to be estimated, Qy(β), is considered. Certain new estimators for the β-quantile have been proposed for any sampling design. The effect of this modification on the standard estimators, ratio, position, stratification, regression and difference type estimators which use the β-quantile of the auxiliary variable to estimate the β-quantile of the main variable, is studied. On the basis of properties derived and some simulation results, the efficiencies of these estimators are compared. It is shown that by the appropriate choice of the α order of the quantile, it is possible to obtain a considerable increase in precision with respect to standard estimators. In simple random sampling, a procedure for choosing the α value is proposed.

Estimators and Confidence Intervals for the Proportion Using Binary Auxiliary Information with Applications to Pharmaceutical Studies

Estimation of a proportion is commonly used in areas such as medicine, biopharmaceutical experiments, etc. Estimation of a proportion using auxiliary information has not been investigated in the literature. Ratio estimators of the population proportion and two-sided confidence intervals based upon auxiliary information are derived in this paper. Real data extracted from the Spanish National Health Survey are used to demonstrate the application of the proposed methods in the estimation of prevalences. Results derived from simulation studies show that proposed estimators are more efficient than the traditional estimator. Proposed confidence intervals outperform the alternative methods, especially in terms of interval width. A study on patients with hypertension is also considered to calculate various estimators and confidence intervals.

Quantile estimation in two-phase sampling

The estimation of quantiles in two-phase sampling with arbitrary sampling design in each of the two phases is investigated. Several ratio and exponentiation type estimators that provide the optimum estimate of a quantile based on an optimum exponent @a are proposed. Properties of these estimators are studied under large sample size approximation and the use of double sampling for stratification to estimate quantiles can also be seen. The real performance of these estimators will be evaluated for the three quartiles on the basis of data from two real populations using different sampling designs. The simulation study shows that proposed estimators can be very satisfactory in terms of relative bias and efficiency.

Using multiparametric auxiliary information at the estimation stage

Difference type estimators use auxiliary information based on an auxiliary parameter (specifically the parameter of interest), associated with the auxiliary variable. In practice, however, several parameters for auxiliary variables are available. This paper discusses how such estimators can be modified to improve the usual methods if information related to other parameters associated with an auxiliary variable or variables is available. Some applications estimating several such parameters are described. A proper set of simulation-based comparisons is made.

Calibration estimation in dual-frame surveys

Survey statisticians make use of auxiliary information to improve estimates. One important example is calibration estimation, which constructs new weights that match benchmark constraints on auxiliary variables while remaining “close” to the design weights. Multiple-frame surveys are increasingly used by statistical agencies and private organizations to reduce sampling costs and/or avoid frame undercoverage errors. Several ways of combining estimates derived from such frames have been proposed elsewhere; in this paper, we extend the calibration paradigm, previously used for single-frame surveys, to calculate the total value of a variable of interest in a dual-frame survey. Calibration is a general tool that allows to include auxiliary information from two frames. It also incorporates, as a special case, certain dual-frame estimators that have been proposed previously. The theoretical properties of our class of estimators are derived and discussed, and simulation studies conducted to compare the efficiency of the procedure, using different sets of auxiliary variables. Finally, the proposed methodology is applied to real data obtained from the Barometer of Culture of Andalusia survey.