In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

Energy is a vital input for social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. Formulation of an energy model will help in the proper allocation of widely available renewable energy sources such as solar, wind, bioenergy and small hydropower in meeting the future energy demand in India. During the last decade several new concepts of energy planning and management such as decentralized planning, energy conservation through improved technologies, waste recycling, integrated energy planning, introduction of renewable energy sources and energy forecasting have emerged. In this paper an attempt has been made to understand and review the various emerging issues related to the energy modeling. The different types of models such as energy planning models, energy supply–demand models, forecasting models, renewable energy models, emission reduction models, optimization models have been reviewed and presented. Also, models based on neural network and fuzzy theory have been reviewed and discussed. The review paper on energy modeling will help the energy planners, researchers and policy makers widely.

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A review of energy models

In the ridge regression analysis, the estimation of ridge parameter k is an important problem. Many methods are available for estimating such a parameter. This article has considered some of these methods and also proposed some new estimators based on generalized ridge regression approach. A simulation study has been made to evaluate the performance of proposed estimators based on the minimum mean squared error (MSE) criterion. The simulation study indicates that under certain conditions the proposed estimators perform well compared to least squares estimators (LSE) and other popular existing estimators. Finally, a numerical example has been analyzed and its findings support the simulation results to some extent.

Performance of some new ridge regression estimators

Energy inputs and crop yield relationship in greenhouse tomato production

An analysis of energy use and input costs for cotton production in Turkey

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Advanced Sampling Theory with Applications

A comprehensive expose of basic and advanced sampling techniques along with their applications in the diverse fields of science and technology. This book is a multi-purpose document. It can be used as a text by teachers, as a reference manual by researchers, and as a practical guide by statisticians. It covers 1165 references from different research journals through almost 1900 citations across 1194 pages, a large number of complete proofs of theorems, important results such as corollaries, and 324 unsolved exercises from several research papers. It includes 159 solved, data-based, real life numerical examples in disciplines such as Agriculture, Demography, Social Science, Applied Economics, Engineering, Medicine, and Survey Sampling. These solved examples are very useful for an understanding of the applications of advanced sampling theory in our daily life and in diverse fields of science. An additional 173 unsolved practical problems are given at the end of the chapters. University and college professors may find these useful when assigning exercises to students. Each exercise gives exposure to several complete research papers for researchers/students. The data-based problems show statisticians how to select a sample and obtain estimates of parameters from a given population by using different sampling strategies, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Derivations of calibration weights from the design weights under single phase and two-phase sampling have been provided for simple numerical examples. These examples will be useful to understand the meaning of benchmarks to improve the design weights. These examples also explain the background of well-known scientific computer packages like CALMAR, GES, SAS, STATA, and SUDAAN etc., used to generate calibration weights by most organizations in the public and private sectors. The ideas of hot deck, cold deck, mean method of imputation, ratio method of imputation, compromised imputation, and multiple imputations have been explained with very simple numerical examples. Simple examples are also provided to understand Jackknife variance estimation under single phase, two-phase [or random non-response by following Sitter (1997)] and multi-stage stratified designs. This book also covers, in a very simple and compact way, many new topics not yet available in any book on the international market. A few of these interesting topics are: median estimation under single phase and two-phase sampling, difference between low level and higher level calibration approach, calibration weights and design weights, estimation of parametric functions, hidden gangs in finite populations, compromised imputation, variance estimation using distinct units, general class of estimators of population mean and variance, wider class of estimators of population mean and variance, power transformation estimators, estimators based on the mean of non-sampled units of the auxiliary character, ratio and regression type estimators for estimating finite population variance similar to proposed by Isaki (1982), unbiased estimators of mean and variance under Midzuno's scheme of sampling, usual and modified jackknife variance estimator, estimation of regression coefficient, concept of revised selection probabilities, multi-character surveys sampling, overlapping, adaptive, and post cluster sampling, new techniques in systematic sampling, successive sampling, small area estimation, continuous populations, and estimation of measurement errors.

Advanced Sampling Theory with Applications: How Michael "selected" Amy

Estimation of sensitivity level of personal interview survey questions

Simple Random Sampling (SRS) is the simplest and most common method of selecting a sample, in which the sample is selected unit by unit, with equal probability of selection for each unit at each draw. In other words, simple random sampling is a method of selecting a sample s of n units from a population Ω of size N by giving equal probability of selection to all units. It is a sampling scheme in which all possible combinations of n units may be formed from the population of N units with the same chance of selection.

Simple Random Sampling

Summary. We suggest an efficient randomized response model that can easily be adjusted to be more efficient than the Warner, Mangat and Singh, and Mangat methods by selecting certain parameters of the proposed randomization device.

Sarjinder Singh

Papers

Advanced Sampling Theory with Applications

Advanced Sampling Theory with Applications: How Michael "selected" Amy

Estimation of sensitivity level of personal interview survey questions

Simple Random Sampling

A new randomized response model