103,654 research outputs found
Joshua Davis: Author of Spare Parts
No full textCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Interview with George Kitamura and Tonia Imai Kitamura, 1979.
No full text"San Benito, Texas
George Kitamura and Tonia Imai Kitamura tell of family history farming in Rio Grande Valley, experiences during the Depression and World War II
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
MNP: R Package for Fitting the Multinomial Probit Model
No full textMNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005).
k-Group Multiple Alignment Based on A* Search
No full textThe multiple alignment of the sequences of DNA and proteins is applied to various important fields in bioscience. Due to its importance, there have been proposed many algorithms, and yet new techniques to resolve difficulties in solving large-scale problems are required. This paper proposes a k-group alignment algorithm for multiple alignment as a practical method. In iterative improvement methods for multiple alignment, the socalled group-to-group two-dimensional dynamic programming has been used, and in this respect our proposal is to extend the ordinary two-group dynamic programming to a k- group alignment programming. This extension is conceptually straightforward, and here our contribution is to demonstrate that the k-group alignment can be implemented so as to run in a reasonable time and space under standard computing environments. This is established by generalizing the A 3 search approach for multiple alignment devised by Ikeda and Imai [8]. The k-group alignment method can..
Option values, switches, and wages: An analysis of the employment guarantee scheme in India
No full textConsistent with real option theory, the authors argue that the value of the Employment Guarantee Scheme (EGS) in rural India and its impact on workers' behavior does not depend so much on its income supplementation as on enlargement of opportunities in the uncertain local labor market. The choice between the EGS and other activities is modeled in a dynamic optimization framework, taking into account a fixed wage rate and certainty of employment under the EGS and a stochastic wage rate under other activities. Specifically, volatility of wages in the rural labor markets has important implications for switches into the EGS and for concomitant welfare effects. Under such conditions, the higher the EGS wage, the greater is its attractiveness to relatively skilled and affluent workers, and for those already in it to continue. These and related predictions of the model are validated by panel data estimation. © 2009 Blackwell Publishing Ltd
Enhanced A∗ algorithms for multiple alignments: optimal alignments for several sequences and k-opt approximate alignments for large cases
No full textAbstractThe multiple alignment of the sequences of DNA and proteins is applicable to various important fields in molecular biology. Although the approach based on Dynamic Programming is well-known for this problem, it requires enormous time and space to obtain the optimal alignment. On the other hand, this problem corresponds to the shortest path problem and the A∗ algorithm, which can efficiently find the shortest path with an estimator, is usable.First, this paper directly applies the A∗ algorithm to multiple sequence alignment problem with more powerful estimator in more than two-dimensional case and discusses the extensions of this approach utilizing an upper bound of the shortest path length and of modification of network structure. The algorithm to provide the upper bound is also proposed in this paper. The basic part of these results was originally shown in Ikeda and Imai [11]. This part is similar to the branch-and-bound techniques implemented in MSA program in Gupta et al. [6]. Our framework is based on the edge length transformation to reduce the problem to the shortest path problem, which is more suitable to generalizations to enumerating suboptimal alignments and parametric analysis as done in Shibuya and Imai [15–17]. By this enhanced A∗ algorithm, optimal multiple alignments of several long sequences can be computed in practice, which is shown by computational results.Second, this paper proposes a k-group alignment algorithm for multiple alignment as a practical method for much larger-size problem of, say multiple alignments of 50–100 sequences. A basic part of these results were originally presented in Imai and Ikeda [13]. In existing iterative improvement methods for multiple alignment, the so-called group-to-group two-dimensional dynamic programming has been used, and in this respect our proposal is to extend the ordinary two-group dynamic programming to a k-group alignment programming. This extension is conceptually straightforward, and here our contribution is to demonstrate that the k-group alignment can be implemented so as to run in a reasonable time and space under standard computing environments. This is established by generalizing the above A∗ search approach. The k-group alignment method can be directly incorporated in existing methods such as iterative improvement algorithms [2, 5] and tree-based (iterative) algorithms [9]. This paper performs computational experiments by applying the k-group method to iterative improvement algorithms, and shows that our approach can find better alignments in reasonable time. For example, through larger-scale computational experiments here, 34 protein sequences with very high homology can be optimally 10-group aligned, and 64 sequences with high homology can be optimally 5-group aligned
Does the employment guarantee scheme stabilise household incomes in rural India?
No full textOur analysis, based on the ICRISAT panel survey of villages in the semi-arid region of south India, confirms the income stabilizing effect of the Employment Guarantee Scheme in India. Variability of household income is measured by an unconditional variance of residuals of an income equation. A (variant) of Heckman's sample selection model is employed to allow for the endogeneity of EGS participation and to assess its income stabilizing role. The (instrumented) EGS participation reduces the residual variance of household income, implying consequent income stabilization
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
No full textMatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
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