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Publications

2000

OMONDI, MRMISANGOQUIRENEBERNARD.  2000.  Design of a Multi-Purpose Screw Jack. A technical Report, 2000. (Revised 2002). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.

1999

OMONDI, MRMISANGOQUIRENEBERNARD.  1999.  Design of a Bicycle Trailer. A Technical Report, 1998. (Revised 1999, and 2001). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1999.  Locally Made Wind pumping System. A technical Report, 1987 (Revised 1992, 1997, 1999). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.

1996

OMONDI, MRMISANGOQUIRENEBERNARD.  1996.  Design of Micro-Geothermal Power Plant. A Technical Report, 1996. (Revised 1998). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.

1994

OMONDI, MRMISANGOQUIRENEBERNARD.  1994.  Design of Micro-Hydropower System for use in Small Rivers. A Technical Report, 1990 (Revised in 1992, 1994). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1988.  Locally Made Woodworking Machine. A Technical Report, 1985 (Revised 1988). Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1987.  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. Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1986.  Locally Made Laundry Machine. A technical Report, 1986. Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1985.  M. Sc. Thesis entitled: THE DEVELOPMENT OF A SELF-OPTIMIZING COUPLING FOR A WINDPUMP. University of Nairobi, 1985. Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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.  1981.  Paper entitled: ENGINEERRING EDUCATION FOR DEVELOPMENT. Presented at the First Seminar on Training of Graduate Engineers, E.R.B., Nairobi. 25. Far East J. of Theo. Stat. 18 (2), pp. 161 . : Far East Journal of Theoretical Statistics 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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