Publications

Found 10 results

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2000
OMONDI MRMISANGOQUIRENEBERNARD. "Design of a Multi-Purpose Screw Jack. A technical Report, 2000. (Revised 2002).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 2000. Abstract
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1999
OMONDI MRMISANGOQUIRENEBERNARD. "Design of a Bicycle Trailer. A Technical Report, 1998. (Revised 1999, and 2001).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1999. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
OMONDI MRMISANGOQUIRENEBERNARD. "Locally Made Wind pumping System. A technical Report, 1987 (Revised 1992, 1997, 1999).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1999. Abstract
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1996
OMONDI MRMISANGOQUIRENEBERNARD. "Design of Micro-Geothermal Power Plant. A Technical Report, 1996. (Revised 1998).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1996. Abstract
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1994
OMONDI MRMISANGOQUIRENEBERNARD. "Design of Micro-Hydropower System for use in Small Rivers. A Technical Report, 1990 (Revised in 1992, 1994).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1994. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
1988
OMONDI MRMISANGOQUIRENEBERNARD. "Locally Made Woodworking Machine. A Technical Report, 1985 (Revised 1988).". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1988. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
1987
OMONDI MRMISANGOQUIRENEBERNARD. "Paper entitled: THE ROLE OF UNIVERSITIES IN ACCELERATING DEVELOPMENT OF INDUSTRY THROUGH RESEARCH AND DISSEMINATION OF INFORMATION: EXAMPLES, DEPARTMENT OF MECHANICAL ENGINEERING. Presented at the Seminar organized by Kenya Industrial Research and Develop.". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1987. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
1986
OMONDI MRMISANGOQUIRENEBERNARD. "Locally Made Laundry Machine. A technical Report, 1986.". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1986. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
1985
OMONDI MRMISANGOQUIRENEBERNARD. "M. Sc. Thesis entitled: THE DEVELOPMENT OF A SELF-OPTIMIZING COUPLING FOR A WINDPUMP. University of Nairobi, 1985.". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1985. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.
1981
OMONDI MRMISANGOQUIRENEBERNARD. "Paper entitled: ENGINEERRING EDUCATION FOR DEVELOPMENT. Presented at the First Seminar on Training of Graduate Engineers, E.R.B., Nairobi. 25.". In: Far East J. of Theo. Stat. 18 (2), pp. 161 . Far East Journal of Theoretical Statistics; 1981. Abstract
v:* {behavior:url(#default#VML);} o:* {behavior:url(#default#VML);} w:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 12.00 Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Common nearly best linear estimates of location and scale parameters of normal and logistic distributions, which are based on complete samples, are considered. Here the population from which the samples are drawn is either normal or logistic population or a fusion of both distributions and the estimates are computed when it is not yet known which of the two populations (between the normal and logistic) is true. The problem discussed in this paper involves two possible population types in a given sample. Samples of sizes  and  are used to validate these estimates and a comparison of their variances is made with those of the best linear unbiased estimators (BLUEs) for normal and logistic distributions.

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