Two new models for survey sampling with sensitive characteristic: design and analysis

Sensitive topics or highly personal questions are often being asked in medical, psychological and sociological surveys. This paper proposes two new models (namely, the triangular and crosswise models) for survey sampling with the sensitive characteristics. We derive the maximum likelihood estimates (MLEs) and large-sample confidence intervals for the proportion of persons with sensitive characteristic. The modified MLEs and their asymptotic properties are developed. Under certain optimality criteria, the designs for the cooperative parameter are provided and the sample size formulas are given. We compare the efficiency of the two models based on the variance criterion. The proposed models have four advantages: neither model requires randomizing device, the models are easy to be implemented for both interviewer and interviewee, the interviewee does not face any sensitive questions, and both models can be applied to both face-to-face personal interviews and mail questionnaires.     

A new measure of association between random variables

We propose a new measure of association between two continuous random variables X and Y based on the covariance between X and the log-odds rate associated to Y. The proposed index of correlation lies in the range [\(-1\), 1]. We show that the extremes of the range, i.e., \(-1\) and 1, are attainable by the Fr\(\acute{\mathrm{e}}\)chet bivariate minimal and maximal distributions, respectively. It is also shown that if X and Y have bivariate normal distribution, the resulting measure of correlation equals the Pearson correlation coefficient \(\rho \). Some interpretations and relationships to other variability measures are presented. Among others, it is shown that for non-negative random variables the proposed association measure can be represented in terms of the mean residual and mean inactivity functions. Some illustrative examples are also provided.     

Forecasting macroeconomic variables using disaggregate survey data

We construct factor models based on disaggregate survey data for forecasting national aggregate macroeconomic variables. Our methodology applies regional and sectoral factor models to Norges Bank’s regional survey and to the Swedish Business Tendency Survey. The analysis identifies which of the pieces of information extracted from the individual regions in Norges Bank’s survey and the sectors for the two surveys perform particularly well at forecasting different variables at various horizons. The results show that several factor models beat an autoregressive benchmark in forecasting inflation and the unemployment rate. However, the factor models are most successful at forecasting GDP growth. Forecast combinations using the past performances of regional and sectoral factor models yield the most accurate forecasts in the majority of the cases.     

When product type experimental design is optimal? Brief survey and new results

In the paper the problem of optimum experimental design for estimating parameters of multivariate regression functions is considered. We address the question: under what conditions one can compose the optimal design from partial designs, obtained by considering partial regressions, which depend on reduced number of variables. After reinterpreting and reviewing briefly existing results we provide some new conditions.     

论设计调查与产品设计的关系

随着市场经济的发展,我们的产品已不只是停留在满足基本功能,而是加入了更多的人性化设计。现在,设计师们深入市场,对产品进行设计前的调查,已经成为产品成功的重要因素。     

一种用于新型电子围栏的低压告警模块的设计

文章介绍了一种用于新型脉冲电子围栏的低压告警模块的设计,给出了设计思路和方法,并画出了相关硬件电路的设计框图。     

Proposing new variables for the identification of strategic groups in franchising

The identification of strategic groups in the Spanish franchising area is the main aim of this study. The authors have added some new strategic variables (not used before) to the study and have classified franchisors between sectors and distribution strategy. The results reveal the existence of four perfectly differentiated strategic groups (types of franchisors). One of the major implications of this study is that the variables that build a strategic group vary depending on the respective sector the network operates in and its distribution strategy. This fact indicates that including sector and distribution strategy is absolutely necessary to achieve good classifications of franchisor types.     

A note on acceptance sampling for variables

For 60 years acceptance sampling by variables has been recommended as an economic alternative to attribute sampling. Entry in the industrial practice happened after these plans were adopted in 1957 as Military Standard 414 by the US Army. Nowadays a slightly modified version of Mil-Std-414 is known as ANSI/ASQC Z1.9 or as International Standard ISO 3951. In this paper it is shown that the derivation of sampling plans by variables is based on assumptions which don’t hold, that acceptance sampling by variables is inappropriate, and that it doesn’t constitute an alternative to attribute sampling.     

A new sequential design based on the robbins-monro procedure

Summary A sequential estimation procedure based on the Robbins-Monro process is proposed. Through observations of a random variableY(x), estimates of a regression functionM(x) are obtained, whereM(x)=E[Y(x)]. The estimates of the roots ofM(x) are shown to be strongly convergent and asymptotically normal with minimum variance.     

A heuristic algorithm for capacity sensitive requirements planning

Available lot sizing rules for use in MRP (Material Requirements Planning) systems ignore capacity limitations at various work centers when sizing future orders. Planned order releases are instead determined by the tradeoff only between the item's set up and inventory holding costs. This limitation can cause unanticipated overloads and underloads at the various work centers, along with higher inventories, poorer customer service, and excessive overtime.This research explores one way to make MRP systems more sensitive to capacity limitations at the time of each regeneration run. A relatively simple heuristic algorithm is designed for this purpose. The procedure is applied to those planned order releases that standard MRP logic identifies as mature for release. The lot sizes for a small percentage of these items are increased or decreased so as to have the greatest impact in smoothing capacity requirements at the various work centers in the system. This algorithm for better integrating material requirements plans and capacity requirements plans is tested with a large scale simulator in a variety of manufacturing environments. This simulator has subsequently undergone extensive tests, including its successful validation with actual data at a large plant of major corporations.Simulation results show that the algorithm's modest extension to MRP logic significantly helps overall performance, particularly with customer service. For a wide range of test environments, past due orders were reduced by more than 30% when the algorithm was used. Inventory levels and capacity problems also improved. Not surprisingly, the algorithm helps the most (compared to not using it at all as an MRP enhancement) in environments in which short-term bottlenecks are most severe. Large lot sizes and tight shop capacities are characteristic of these environments. The algorithm works the best when forecast errors are not excessive and the master schedule is not too “nervous.”This proposed procedure is but one step toward making MRP more capacity sensitive. The widely heralded concept of “closed-loop” MRP means that inventory analysts must change or “fix up” parts of the computer generated material requirements plan. What has been missing is a tool for identifying the unrealistic parts of the plan. Our algorithm helps formalize this identification process and singles out a few planned order releases each week. This information comes to the analyst's attention as part of the usual action notices. These pointers to capacity problems go well beyond capacity requirements planning (CRP) and would be impossible without computer assistance.Our study produced two other findings. First, short-term bottlenecks occur even when the master production schedule is leveled. The culprits are the lot sizing choices for items at lower levels in the bills of material. “Rough-cut” capacity planning, such as resource requirements planning, therefore is not a sufficient tool for leveling capacity requirements. It must be supplemented by a way to smooth bottlenecks otherwise caused by shop orders for intermediate items. Second, the disruptive effect of large lot sizes is apparent, both in terms of higher inventories and worse customer service. Large lot sizes not only inflate inventories, but paradoxically hurt customer service because they create more capacity bottlenecks. The only reason why management should prefer large lot sizes is if set-up times are substantial and cannot be efficiently reduced. This finding is very much in step with the current interest in just-in-time (JIT) systems.     

A law of large numbers for abstract random variables

Summary It was emphasized by Fréchet [8] that in many of the practical applications, we get, as a result of a random experiment, an abstract random variableX belonging to a setX. Givenn determinations of the variableX, an arithmetic meanZ _n is introduced in 2-. It reduces to the classical arithmetic mean, whenX is the set of real numbers. Moreover, if the random variableX is Gaussian, having a meanM(X), and belongs to a certain (4-) familyF, the introduced arithmetic meanZ _n constitutes an exhausting estimation [5] of the meanM(X).     

Preference for equivalent random variables: A price for unbounded utilities

Savage's expected utility theory orders acts by the expectation of the utility function for outcomes over states. Therefore, preference between acts depends only on the utilities for outcomes and the probability distribution of states. When acts have more than finitely many possible outcomes, then utility is bounded in Savage's theory. This paper explores consequences of allowing preferences over acts with unbounded utility. Under certain regularity assumptions about indifference, and in order to respect (uniform) strict dominance between acts, there will be a strict preference between some pairs of acts that have the same distribution of outcomes. Consequently in these cases, preference is not a function of utility and probability alone.     

A large factor model for forecasting macroeconomic variables in South Africa

This paper uses large Factor Models (FMs), which accommodate a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contain 267 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quarter-ahead out-of-sample forecasts from 2001Q1 to 2006Q4, indicate that the FMs tend to outperform alternative models such as an unrestricted VAR, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the three variables under consideration, hence indicating the blessings of dimensionality.     

A Bayesian estimator for stochastic frontier models with errors in variables

A Bayesian estimator is proposed for a stochastic frontier model with errors in variables. The model assumes a truncated-normal distribution for the inefficiency and accommodates exogenous determinants of inefficiency. An empirical example of Tobin??s Q investment model is provided, in which the Q variable is known to suffer from measurement error. Results show that correcting for measurement error in the Q variable has an important effect on the estimation results.     

浅析水文地质岩土工程中的勘察设计和施工

从当前我国水文地质岩土工程中勘察设计以及工程施工的实际情况入手,文章提出了促使水文地质岩土工程勘察设计以及工程施工更好开展的完善措施。     

A note on exchangeability of random variables

A sufficient condition for the induced exchangeability or partial exchangeability of linear functions of exchangeable random variables is presented. The use of this result is illustrated through the establishment of conditional exchangeability for two sets of dependent random variables that are important in constructing conditionally distribution–free test procedures for two distinctly different problems.